update

.gitignore

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+**_pycache**

app.py

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+import os
+import signal
+import time
+import csv
+import sys
+import warnings
+import random
+import gradio as gr
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+import torch.multiprocessing as mp
+import numpy as np
+import time
+import pprint
+from loguru import logger
+import smplx
+from torch.utils.tensorboard import SummaryWriter
+import wandb
+import matplotlib.pyplot as plt
+from utils import config, logger_tools, other_tools_hf, metric, data_transfer, other_tools
+from dataloaders import data_tools
+from dataloaders.build_vocab import Vocab
+from optimizers.optim_factory import create_optimizer
+from optimizers.scheduler_factory import create_scheduler
+from optimizers.loss_factory import get_loss_func
+from dataloaders.data_tools import joints_list
+from utils import rotation_conversions as rc
+import soundfile as sf
+import librosa 
+import subprocess
+from transformers import pipeline
+from diffusion.model_util import create_gaussian_diffusion
+from diffusion.resample import create_named_schedule_sampler
+from models.vq.model import RVQVAE
+import train
+import spaces
+
+command = ["bash","./demo/install_mfs.sh"]
+result = subprocess.run(command, capture_output=True, text=True)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+pipe = pipeline(
+  "automatic-speech-recognition",
+  model="openai/whisper-tiny.en",
+  chunk_length_s=30,
+  device=device,
+)       
+
+debug = False
+
+class BaseTrainer(object):
+    def __init__(self, args,ap):
+        args.use_ddim=True
+        hf_dir = "hf"
+        time_local = time.localtime()
+        time_name_expend = "%02d%02d_%02d%02d%02d_"%(time_local[1], time_local[2],time_local[3], time_local[4], time_local[5])
+        self.time_name_expend = time_name_expend
+        tmp_dir = args.out_path + "custom/"+ time_name_expend + hf_dir
+        if not os.path.exists(tmp_dir + "/"):
+            os.makedirs(tmp_dir + "/")
+        self.audio_path = tmp_dir + "/tmp.wav"
+        sf.write(self.audio_path, ap[1], ap[0])
+        
+        
+        audio, ssr = librosa.load(self.audio_path,sr=args.audio_sr)
+
+        # use asr model to get corresponding text transcripts
+        file_path = tmp_dir+"/tmp.lab"
+        self.textgrid_path = tmp_dir + "/tmp.TextGrid"
+        if not debug:
+            text = pipe(audio, batch_size=8)["text"]
+            with open(file_path, "w", encoding="utf-8") as file:
+                file.write(text)
+            
+            # use montreal forced aligner to get textgrid
+            
+            command = ["mfa", "align", tmp_dir, "english_us_arpa", "english_us_arpa", tmp_dir]
+            result = subprocess.run(command, capture_output=True, text=True)
+            
+
+        ap = (ssr, audio)
+        self.args = args
+        self.rank = 0 # dist.get_rank()
+       
+        args.textgrid_file_path = self.textgrid_path
+        args.audio_file_path = self.audio_path
+    
+    
+        self.rank = 0 # dist.get_rank()
+       
+        self.checkpoint_path = tmp_dir
+        args.tmp_dir = tmp_dir
+        if self.rank == 0:
+            self.test_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "test")
+            self.test_loader = torch.utils.data.DataLoader(
+                self.test_data, 
+                batch_size=1,  
+                shuffle=False,  
+                num_workers=args.loader_workers,
+                drop_last=False,
+            )
+        logger.info(f"Init test dataloader success")
+        model_module = __import__(f"models.{args.model}", fromlist=["something"])
+        
+        self.model = torch.nn.DataParallel(getattr(model_module, args.g_name)(args), args.gpus).cuda()
+        
+        if self.rank == 0:
+            logger.info(self.model)
+            logger.info(f"init {args.g_name} success")
+
+        self.smplx = smplx.create(
+        self.args.data_path_1+"smplx_models/", 
+            model_type='smplx',
+            gender='NEUTRAL_2020', 
+            use_face_contour=False,
+            num_betas=300,
+            num_expression_coeffs=100, 
+            ext='npz',
+            use_pca=False,
+        ).to(self.rank).eval()    
+
+
+
+    
+    
+        self.args = args
+        self.ori_joint_list = joints_list[self.args.ori_joints]
+        self.tar_joint_list_face = joints_list["beat_smplx_face"]
+        self.tar_joint_list_upper = joints_list["beat_smplx_upper"]
+        self.tar_joint_list_hands = joints_list["beat_smplx_hands"]
+        self.tar_joint_list_lower = joints_list["beat_smplx_lower"]
+       
+        self.joint_mask_face = np.zeros(len(list(self.ori_joint_list.keys()))*3)
+        self.joints = 55
+        for joint_name in self.tar_joint_list_face:
+            self.joint_mask_face[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        self.joint_mask_upper = np.zeros(len(list(self.ori_joint_list.keys()))*3)
+        for joint_name in self.tar_joint_list_upper:
+            self.joint_mask_upper[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        self.joint_mask_hands = np.zeros(len(list(self.ori_joint_list.keys()))*3)
+        for joint_name in self.tar_joint_list_hands:
+            self.joint_mask_hands[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        self.joint_mask_lower = np.zeros(len(list(self.ori_joint_list.keys()))*3)
+        for joint_name in self.tar_joint_list_lower:
+            self.joint_mask_lower[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+
+        self.tracker = other_tools.EpochTracker(["fid", "l1div", "bc", "rec", "trans", "vel", "transv", 'dis', 'gen', 'acc', 'transa', 'exp', 'lvd', 'mse', "cls", "rec_face", "latent", "cls_full", "cls_self", "cls_word", "latent_word","latent_self","predict_x0_loss"], [False,True,True, False, False, False, False, False, False, False, False, False, False, False, False, False, False,False, False, False,False,False,False])
+            
+        vq_model_module = __import__(f"models.motion_representation", fromlist=["something"])
+        self.args.vae_layer = 2
+        self.args.vae_length = 256
+        self.args.vae_test_dim = 106
+        self.vq_model_face = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank)
+        other_tools.load_checkpoints(self.vq_model_face, "./datasets/hub/pretrained_vq/face_vertex_1layer_790.bin", args.e_name)
+        
+        
+        vq_type = self.args.vqvae_type
+        if vq_type=="vqvae":
+            
+            self.args.vae_layer = 4
+            self.args.vae_test_dim = 78
+            self.vq_model_upper = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank)
+            other_tools.load_checkpoints(self.vq_model_upper, args.vqvae_upper_path, args.e_name)
+            self.args.vae_test_dim = 180
+            self.vq_model_hands = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank)
+            other_tools.load_checkpoints(self.vq_model_hands, args.vqvae_hands_path, args.e_name)
+            self.args.vae_test_dim = 54
+            self.args.vae_layer = 4
+            self.vq_model_lower = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank)
+            other_tools.load_checkpoints(self.vq_model_lower, args.vqvae_lower_path, args.e_name)
+            
+        elif vq_type=="rvqvae":
+
+            args.num_quantizers = 6
+            args.shared_codebook =  False
+            args.quantize_dropout_prob = 0.2
+            args.mu = 0.99
+
+            args.nb_code = 512
+            args.code_dim = 512
+            args.code_dim = 512
+            args.down_t = 2
+            args.stride_t = 2
+            args.width = 512
+            args.depth = 3
+            args.dilation_growth_rate = 3
+            args.vq_act = "relu"
+            args.vq_norm = None
+                                
+            dim_pose = 78  
+            args.body_part = "upper"
+            self.vq_model_upper = RVQVAE(args,
+                                dim_pose,
+                                args.nb_code,
+                                args.code_dim,
+                                args.code_dim,
+                                args.down_t,
+                                args.stride_t,
+                                args.width,
+                                args.depth,
+                                args.dilation_growth_rate,
+                                args.vq_act,
+                                args.vq_norm)
+
+            dim_pose = 180
+            args.body_part = "hands"
+            self.vq_model_hands = RVQVAE(args,
+                                dim_pose,
+                                args.nb_code,
+                                args.code_dim,
+                                args.code_dim,
+                                args.down_t,
+                                args.stride_t,
+                                args.width,
+                                args.depth,
+                                args.dilation_growth_rate,
+                                args.vq_act,
+                                args.vq_norm)
+
+            dim_pose = 54
+            if args.use_trans:
+                dim_pose = 57
+                self.args.vqvae_lower_path = self.args.vqvae_lower_trans_path
+            args.body_part = "lower"
+            self.vq_model_lower = RVQVAE(args,
+                                dim_pose,
+                                args.nb_code,
+                                args.code_dim,
+                                args.code_dim,
+                                args.down_t,
+                                args.stride_t,
+                                args.width,
+                                args.depth,
+                                args.dilation_growth_rate,
+                                args.vq_act,
+                                args.vq_norm)
+                    
+            self.vq_model_upper.load_state_dict(torch.load(self.args.vqvae_upper_path)['net'])
+            self.vq_model_hands.load_state_dict(torch.load(self.args.vqvae_hands_path)['net'])
+            self.vq_model_lower.load_state_dict(torch.load(self.args.vqvae_lower_path)['net'])
+            
+            self.vqvae_latent_scale = self.args.vqvae_latent_scale 
+
+            self.vq_model_upper.eval().to(self.rank)
+            self.vq_model_hands.eval().to(self.rank)
+            self.vq_model_lower.eval().to(self.rank)
+            
+        
+        
+        
+        
+        self.args.vae_test_dim = 61
+        self.args.vae_layer = 4
+        self.args.vae_test_dim = 330
+        self.args.vae_layer = 4
+        self.args.vae_length = 240
+
+
+        self.vq_model_face.eval()
+        self.vq_model_upper.eval()
+        self.vq_model_hands.eval()
+        self.vq_model_lower.eval()
+
+        self.cls_loss = nn.NLLLoss().to(self.rank)
+        self.reclatent_loss = nn.MSELoss().to(self.rank)
+        self.vel_loss = torch.nn.L1Loss(reduction='mean').to(self.rank)
+        self.rec_loss = get_loss_func("GeodesicLoss").to(self.rank) 
+        self.log_softmax = nn.LogSoftmax(dim=2).to(self.rank)
+        
+        self.diffusion = create_gaussian_diffusion(use_ddim=args.use_ddim)
+        self.schedule_sampler_type = 'uniform'
+        self.schedule_sampler = create_named_schedule_sampler(self.schedule_sampler_type, self.diffusion)
+        self.mean = np.load(args.mean_pose_path)
+        self.std = np.load(args.std_pose_path)
+        
+        self.use_trans = args.use_trans
+        if self.use_trans:
+            self.trans_mean = np.load(args.mean_trans_path)
+            self.trans_std = np.load(args.std_trans_path)
+            self.trans_mean = torch.from_numpy(self.trans_mean).cuda()
+            self.trans_std = torch.from_numpy(self.trans_std).cuda()
+        
+
+        joints = [3,6,9,12,13,14,15,16,17,18,19,20,21]
+        upper_body_mask = []
+        for i in joints:
+            upper_body_mask.extend([i*6, i*6+1, i*6+2, i*6+3, i*6+4, i*6+5])
+
+        joints = list(range(25,55))
+        hands_body_mask = []
+        for i in joints:
+            hands_body_mask.extend([i*6, i*6+1, i*6+2, i*6+3, i*6+4, i*6+5])
+
+        joints = [0,1,2,4,5,7,8,10,11]
+        lower_body_mask = []
+        for i in joints:
+            lower_body_mask.extend([i*6, i*6+1, i*6+2, i*6+3, i*6+4, i*6+5])
+
+        self.mean_upper = self.mean[upper_body_mask]
+        self.mean_hands = self.mean[hands_body_mask]
+        self.mean_lower = self.mean[lower_body_mask]
+        self.std_upper = self.std[upper_body_mask]
+        self.std_hands = self.std[hands_body_mask]
+        self.std_lower = self.std[lower_body_mask]
+        
+        self.mean_upper = torch.from_numpy(self.mean_upper).cuda()
+        self.mean_hands = torch.from_numpy(self.mean_hands).cuda()
+        self.mean_lower = torch.from_numpy(self.mean_lower).cuda()
+        self.std_upper = torch.from_numpy(self.std_upper).cuda()
+        self.std_hands = torch.from_numpy(self.std_hands).cuda()
+        self.std_lower = torch.from_numpy(self.std_lower).cuda()
+      
+    
+    def inverse_selection(self, filtered_t, selection_array, n):
+        original_shape_t = np.zeros((n, selection_array.size))
+        selected_indices = np.where(selection_array == 1)[0]
+        for i in range(n):
+            original_shape_t[i, selected_indices] = filtered_t[i]
+        return original_shape_t
+    
+    def inverse_selection_tensor(self, filtered_t, selection_array, n):
+        selection_array = torch.from_numpy(selection_array).cuda()
+        original_shape_t = torch.zeros((n, 165)).cuda()
+        selected_indices = torch.where(selection_array == 1)[0]
+        for i in range(n):
+            original_shape_t[i, selected_indices] = filtered_t[i]
+        return original_shape_t
+    
+    def _load_data(self, dict_data):
+        tar_pose_raw = dict_data["pose"]
+        tar_pose = tar_pose_raw[:, :, :165].to(self.rank)
+        tar_contact = tar_pose_raw[:, :, 165:169].to(self.rank)
+        tar_trans = dict_data["trans"].to(self.rank)
+        tar_trans_v = dict_data["trans_v"].to(self.rank)
+        tar_exps = dict_data["facial"].to(self.rank)
+        in_audio = dict_data["audio"].to(self.rank) 
+        in_word = dict_data["word"].to(self.rank)
+        tar_beta = dict_data["beta"].to(self.rank)
+        tar_id = dict_data["id"].to(self.rank).long()
+        bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints
+
+        tar_pose_jaw = tar_pose[:, :, 66:69]
+        tar_pose_jaw = rc.axis_angle_to_matrix(tar_pose_jaw.reshape(bs, n, 1, 3))
+        tar_pose_jaw = rc.matrix_to_rotation_6d(tar_pose_jaw).reshape(bs, n, 1*6)
+        tar_pose_face = torch.cat([tar_pose_jaw, tar_exps], dim=2)
+
+        tar_pose_hands = tar_pose[:, :, 25*3:55*3]
+        tar_pose_hands = rc.axis_angle_to_matrix(tar_pose_hands.reshape(bs, n, 30, 3))
+        tar_pose_hands = rc.matrix_to_rotation_6d(tar_pose_hands).reshape(bs, n, 30*6)
+
+        tar_pose_upper = tar_pose[:, :, self.joint_mask_upper.astype(bool)]
+        tar_pose_upper = rc.axis_angle_to_matrix(tar_pose_upper.reshape(bs, n, 13, 3))
+        tar_pose_upper = rc.matrix_to_rotation_6d(tar_pose_upper).reshape(bs, n, 13*6)
+
+        tar_pose_leg = tar_pose[:, :, self.joint_mask_lower.astype(bool)]
+        tar_pose_leg = rc.axis_angle_to_matrix(tar_pose_leg.reshape(bs, n, 9, 3))
+        tar_pose_leg = rc.matrix_to_rotation_6d(tar_pose_leg).reshape(bs, n, 9*6)
+
+        tar_pose_lower = tar_pose_leg
+
+
+        tar4dis = torch.cat([tar_pose_jaw, tar_pose_upper, tar_pose_hands, tar_pose_leg], dim=2)
+
+      
+        if self.args.pose_norm:
+            tar_pose_upper = (tar_pose_upper - self.mean_upper) / self.std_upper
+            tar_pose_hands = (tar_pose_hands - self.mean_hands) / self.std_hands
+            tar_pose_lower = (tar_pose_lower - self.mean_lower) / self.std_lower
+        
+        if self.use_trans:
+            tar_trans_v = (tar_trans_v - self.trans_mean)/self.trans_std
+            tar_pose_lower = torch.cat([tar_pose_lower,tar_trans_v], dim=-1)
+      
+        latent_face_top = self.vq_model_face.map2latent(tar_pose_face) # bs*n/4
+        latent_upper_top = self.vq_model_upper.map2latent(tar_pose_upper)
+        latent_hands_top = self.vq_model_hands.map2latent(tar_pose_hands)
+        latent_lower_top = self.vq_model_lower.map2latent(tar_pose_lower)
+        
+        latent_in = torch.cat([latent_upper_top, latent_hands_top, latent_lower_top], dim=2)/self.args.vqvae_latent_scale
+        
+        
+        tar_pose_6d = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3))
+        tar_pose_6d = rc.matrix_to_rotation_6d(tar_pose_6d).reshape(bs, n, 55*6)
+        latent_all = torch.cat([tar_pose_6d, tar_trans, tar_contact], dim=-1)
+        style_feature = None
+        if self.args.use_motionclip:
+            motionclip_feat = tar_pose_6d[...,:22*6]
+            batch = {}
+            bs,seq,feat = motionclip_feat.shape
+            batch['x']=motionclip_feat.permute(0,2,1).contiguous()
+            batch['y']=torch.zeros(bs).int().cuda()
+            batch['mask']=torch.ones([bs,seq]).bool().cuda()
+            style_feature = self.motionclip.encoder(batch)['mu'].detach().float()
+            
+        
+        
+        # print(tar_index_value_upper_top.shape, index_in.shape)
+        return {
+            "tar_pose_jaw": tar_pose_jaw,
+            "tar_pose_face": tar_pose_face,
+            "tar_pose_upper": tar_pose_upper,
+            "tar_pose_lower": tar_pose_lower,
+            "tar_pose_hands": tar_pose_hands,
+            'tar_pose_leg': tar_pose_leg,
+            "in_audio": in_audio,
+            "in_word": in_word,
+            "tar_trans": tar_trans,
+            "tar_exps": tar_exps,
+            "tar_beta": tar_beta,
+            "tar_pose": tar_pose,
+            "tar4dis": tar4dis,
+            "latent_face_top": latent_face_top,
+            "latent_upper_top": latent_upper_top,
+            "latent_hands_top": latent_hands_top,
+            "latent_lower_top": latent_lower_top,
+            "latent_in":  latent_in,
+            "tar_id": tar_id,
+            "latent_all": latent_all,
+            "tar_pose_6d": tar_pose_6d,
+            "tar_contact": tar_contact,
+            "style_feature":style_feature,
+        }
+    
+    def _g_test(self, loaded_data):
+        sample_fn = self.diffusion.p_sample_loop
+        if self.args.use_ddim:
+            sample_fn = self.diffusion.ddim_sample_loop
+        mode = 'test'
+        bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], self.joints 
+        tar_pose = loaded_data["tar_pose"]
+        tar_beta = loaded_data["tar_beta"]
+        tar_exps = loaded_data["tar_exps"]
+        tar_contact = loaded_data["tar_contact"]
+        tar_trans = loaded_data["tar_trans"]
+        in_word = loaded_data["in_word"]
+        in_audio = loaded_data["in_audio"]
+        in_x0 = loaded_data['latent_in']
+        in_seed = loaded_data['latent_in']
+        
+        remain = n%8
+        if remain != 0:
+            tar_pose = tar_pose[:, :-remain, :]
+            tar_beta = tar_beta[:, :-remain, :]
+            tar_trans = tar_trans[:, :-remain, :]
+            in_word = in_word[:, :-remain]
+            tar_exps = tar_exps[:, :-remain, :]
+            tar_contact = tar_contact[:, :-remain, :]
+            in_x0 = in_x0[:, :in_x0.shape[1]-(remain//self.args.vqvae_squeeze_scale), :]
+            in_seed = in_seed[:, :in_x0.shape[1]-(remain//self.args.vqvae_squeeze_scale), :]
+            n = n - remain
+
+        tar_pose_jaw = tar_pose[:, :, 66:69]
+        tar_pose_jaw = rc.axis_angle_to_matrix(tar_pose_jaw.reshape(bs, n, 1, 3))
+        tar_pose_jaw = rc.matrix_to_rotation_6d(tar_pose_jaw).reshape(bs, n, 1*6)
+        tar_pose_face = torch.cat([tar_pose_jaw, tar_exps], dim=2)
+
+        tar_pose_hands = tar_pose[:, :, 25*3:55*3]
+        tar_pose_hands = rc.axis_angle_to_matrix(tar_pose_hands.reshape(bs, n, 30, 3))
+        tar_pose_hands = rc.matrix_to_rotation_6d(tar_pose_hands).reshape(bs, n, 30*6)
+
+        tar_pose_upper = tar_pose[:, :, self.joint_mask_upper.astype(bool)]
+        tar_pose_upper = rc.axis_angle_to_matrix(tar_pose_upper.reshape(bs, n, 13, 3))
+        tar_pose_upper = rc.matrix_to_rotation_6d(tar_pose_upper).reshape(bs, n, 13*6)
+
+        tar_pose_leg = tar_pose[:, :, self.joint_mask_lower.astype(bool)]
+        tar_pose_leg = rc.axis_angle_to_matrix(tar_pose_leg.reshape(bs, n, 9, 3))
+        tar_pose_leg = rc.matrix_to_rotation_6d(tar_pose_leg).reshape(bs, n, 9*6)
+        tar_pose_lower = torch.cat([tar_pose_leg, tar_trans, tar_contact], dim=2)
+        
+        tar_pose_6d = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3))
+        tar_pose_6d = rc.matrix_to_rotation_6d(tar_pose_6d).reshape(bs, n, 55*6)
+        latent_all = torch.cat([tar_pose_6d, tar_trans, tar_contact], dim=-1)
+        
+        rec_all_face = []
+        rec_all_upper = []
+        rec_all_lower = []
+        rec_all_hands = []
+        vqvae_squeeze_scale = self.args.vqvae_squeeze_scale
+        roundt = (n - self.args.pre_frames * vqvae_squeeze_scale) // (self.args.pose_length - self.args.pre_frames * vqvae_squeeze_scale)
+        remain = (n - self.args.pre_frames * vqvae_squeeze_scale) % (self.args.pose_length - self.args.pre_frames * vqvae_squeeze_scale)
+        round_l = self.args.pose_length - self.args.pre_frames * vqvae_squeeze_scale
+         
+
+        for i in range(0, roundt):
+            in_word_tmp = in_word[:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames * vqvae_squeeze_scale]
+
+            in_audio_tmp = in_audio[:, i*(16000//30*round_l):(i+1)*(16000//30*round_l)+16000//30*self.args.pre_frames * vqvae_squeeze_scale]
+            in_id_tmp = loaded_data['tar_id'][:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames]
+            in_seed_tmp = in_seed[:, i*(round_l)//vqvae_squeeze_scale:(i+1)*(round_l)//vqvae_squeeze_scale+self.args.pre_frames]
+            in_x0_tmp = in_x0[:, i*(round_l)//vqvae_squeeze_scale:(i+1)*(round_l)//vqvae_squeeze_scale+self.args.pre_frames]
+            mask_val = torch.ones(bs, self.args.pose_length, self.args.pose_dims+3+4).float().cuda()
+            mask_val[:, :self.args.pre_frames, :] = 0.0
+            if i == 0:
+                in_seed_tmp = in_seed_tmp[:, :self.args.pre_frames, :]
+            else:
+                in_seed_tmp = last_sample[:, -self.args.pre_frames:, :]
+
+            cond_ = {'y':{}}
+            cond_['y']['audio'] = in_audio_tmp
+            cond_['y']['word'] = in_word_tmp
+            cond_['y']['id'] = in_id_tmp
+            cond_['y']['seed'] =in_seed_tmp
+            cond_['y']['mask'] = (torch.zeros([self.args.batch_size, 1, 1, self.args.pose_length]) < 1).cuda()
+            
+
+        
+            cond_['y']['style_feature'] = torch.zeros([bs, 512]).cuda()
+
+            shape_ = (bs, 1536, 1, 32)
+            sample = sample_fn(
+                self.model,
+                shape_,
+                clip_denoised=False,
+                model_kwargs=cond_,
+                skip_timesteps=0,
+                init_image=None,
+                progress=True,
+                dump_steps=None,
+                noise=None,
+                const_noise=False,
+            )
+            sample = sample.squeeze().permute(1,0).unsqueeze(0)
+
+            last_sample = sample.clone()
+            
+            rec_latent_upper = sample[...,:512]
+            rec_latent_hands = sample[...,512:1024]
+            rec_latent_lower = sample[...,1024:1536]
+            
+           
+
+            if i == 0:
+                rec_all_upper.append(rec_latent_upper)
+                rec_all_hands.append(rec_latent_hands)
+                rec_all_lower.append(rec_latent_lower)
+            else:
+                rec_all_upper.append(rec_latent_upper[:, self.args.pre_frames:])
+                rec_all_hands.append(rec_latent_hands[:, self.args.pre_frames:])
+                rec_all_lower.append(rec_latent_lower[:, self.args.pre_frames:])
+
+        rec_all_upper = torch.cat(rec_all_upper, dim=1) * self.vqvae_latent_scale
+        rec_all_hands = torch.cat(rec_all_hands, dim=1) * self.vqvae_latent_scale
+        rec_all_lower = torch.cat(rec_all_lower, dim=1) * self.vqvae_latent_scale
+
+        rec_upper = self.vq_model_upper.latent2origin(rec_all_upper)[0]
+        rec_hands = self.vq_model_hands.latent2origin(rec_all_hands)[0]
+        rec_lower = self.vq_model_lower.latent2origin(rec_all_lower)[0]
+        
+        
+        if self.use_trans:
+            rec_trans_v = rec_lower[...,-3:]
+            rec_trans_v = rec_trans_v * self.trans_std + self.trans_mean
+            rec_trans = torch.zeros_like(rec_trans_v)
+            rec_trans = torch.cumsum(rec_trans_v, dim=-2)
+            rec_trans[...,1]=rec_trans_v[...,1]
+            rec_lower = rec_lower[...,:-3]
+        
+        if self.args.pose_norm:
+            rec_upper = rec_upper * self.std_upper + self.mean_upper
+            rec_hands = rec_hands * self.std_hands + self.mean_hands
+            rec_lower = rec_lower * self.std_lower + self.mean_lower
+
+
+
+
+        n = n - remain
+        tar_pose = tar_pose[:, :n, :]
+        tar_exps = tar_exps[:, :n, :]
+        tar_trans = tar_trans[:, :n, :]
+        tar_beta = tar_beta[:, :n, :]
+
+
+        rec_exps = tar_exps
+        #rec_pose_jaw = rec_face[:, :, :6]
+        rec_pose_legs = rec_lower[:, :, :54]
+        bs, n = rec_pose_legs.shape[0], rec_pose_legs.shape[1]
+        rec_pose_upper = rec_upper.reshape(bs, n, 13, 6)
+        rec_pose_upper = rc.rotation_6d_to_matrix(rec_pose_upper)#
+        rec_pose_upper = rc.matrix_to_axis_angle(rec_pose_upper).reshape(bs*n, 13*3)
+        rec_pose_upper_recover = self.inverse_selection_tensor(rec_pose_upper, self.joint_mask_upper, bs*n)
+        rec_pose_lower = rec_pose_legs.reshape(bs, n, 9, 6)
+        rec_pose_lower = rc.rotation_6d_to_matrix(rec_pose_lower)
+        rec_lower2global = rc.matrix_to_rotation_6d(rec_pose_lower.clone()).reshape(bs, n, 9*6)
+        rec_pose_lower = rc.matrix_to_axis_angle(rec_pose_lower).reshape(bs*n, 9*3)
+        rec_pose_lower_recover = self.inverse_selection_tensor(rec_pose_lower, self.joint_mask_lower, bs*n)
+        rec_pose_hands = rec_hands.reshape(bs, n, 30, 6)
+        rec_pose_hands = rc.rotation_6d_to_matrix(rec_pose_hands)
+        rec_pose_hands = rc.matrix_to_axis_angle(rec_pose_hands).reshape(bs*n, 30*3)
+        rec_pose_hands_recover = self.inverse_selection_tensor(rec_pose_hands, self.joint_mask_hands, bs*n)
+        rec_pose = rec_pose_upper_recover + rec_pose_lower_recover + rec_pose_hands_recover 
+        rec_pose[:, 66:69] = tar_pose.reshape(bs*n, 55*3)[:, 66:69]
+
+        rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs*n, j, 3))
+        rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6)
+        tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs*n, j, 3))
+        tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+        
+        return {
+            'rec_pose': rec_pose,
+            'rec_trans': rec_trans,
+            'tar_pose': tar_pose,
+            'tar_exps': tar_exps,
+            'tar_beta': tar_beta,
+            'tar_trans': tar_trans,
+            'rec_exps': rec_exps,
+        }
+
+
+    def test_demo(self, epoch):
+        '''
+        input audio and text, output motion
+        do not calculate loss and metric
+        save video
+        '''
+        results_save_path = self.checkpoint_path + f"/{epoch}/"
+        if os.path.exists(results_save_path): 
+            import shutil
+            shutil.rmtree(results_save_path)
+        os.makedirs(results_save_path)
+        start_time = time.time()
+        total_length = 0
+        test_seq_list = self.test_data.selected_file
+        align = 0 
+        latent_out = []
+        latent_ori = []
+        l2_all = 0 
+        lvel = 0
+        self.model.eval()
+        self.smplx.eval()
+        # self.eval_copy.eval()
+        with torch.no_grad():
+            for its, batch_data in enumerate(self.test_loader):
+                loaded_data = self._load_data(batch_data)    
+                net_out = self._g_test(loaded_data)
+                tar_pose = net_out['tar_pose']
+                rec_pose = net_out['rec_pose']
+                tar_exps = net_out['tar_exps']
+                tar_beta = net_out['tar_beta']
+                rec_trans = net_out['rec_trans']
+                tar_trans = net_out['tar_trans']
+                rec_exps = net_out['rec_exps']
+                bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints
+                if (30/self.args.pose_fps) != 1:
+                    assert 30%self.args.pose_fps == 0
+                    n *= int(30/self.args.pose_fps)
+                    tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1)
+                    rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1)
+                
+
+                rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs*n, j, 6))
+                rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6)
+                tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs*n, j, 6))
+                tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+
+                rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs*n, j, 6))
+                rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3)
+                tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs*n, j, 6))
+                tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3)
+                
+
+                tar_pose_np = tar_pose.detach().cpu().numpy()
+                rec_pose_np = rec_pose.detach().cpu().numpy()
+                rec_trans_np = rec_trans.detach().cpu().numpy().reshape(bs*n, 3)
+                rec_exp_np = rec_exps.detach().cpu().numpy().reshape(bs*n, 100) 
+                tar_exp_np = tar_exps.detach().cpu().numpy().reshape(bs*n, 100)
+                tar_trans_np = tar_trans.detach().cpu().numpy().reshape(bs*n, 3)
+                gt_npz = np.load("./demo/examples/2_scott_0_1_1.npz", allow_pickle=True)
+
+                results_npz_file_save_path = results_save_path+f"result_{self.time_name_expend[:-1]}"+'.npz'
+                np.savez(results_npz_file_save_path,
+                    betas=gt_npz["betas"],
+                    poses=rec_pose_np,
+                    expressions=rec_exp_np,
+                    trans=rec_trans_np,
+                    model='smplx2020',
+                    gender='neutral',
+                    mocap_frame_rate = 30,
+                )
+                total_length += n
+                render_vid_path = other_tools_hf.render_one_sequence_no_gt(
+                    results_npz_file_save_path, 
+                    # results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', 
+                    results_save_path,
+                    self.audio_path,
+                    self.args.data_path_1+"smplx_models/",
+                    use_matplotlib = False,
+                    args = self.args,
+                    )
+
+        result = [
+            gr.Video(value=render_vid_path, visible=True),
+            gr.File(value=results_npz_file_save_path, label="download motion and visualize in blender"),
+            ]
+        
+        end_time = time.time() - start_time
+        logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion")
+        return result
+       
+@logger.catch
+@spaces.GPU
+def syntalker(audio_path,sample_stratege):
+    args = config.parse_args()
+    if sample_stratege==0:
+        args.use_ddim=True
+    elif sample_stratege==1:
+        args.use_ddim=False
+    print(sample_stratege)
+    print(args.use_ddim)
+    #os.environ['TRANSFORMERS_CACHE'] = args.data_path_1 + "hub/"
+    if not sys.warnoptions:
+        warnings.simplefilter("ignore")
+    # dist.init_process_group(backend="gloo", rank=rank, world_size=world_size)
+
+    #logger_tools.set_args_and_logger(args, rank)
+    other_tools_hf.set_random_seed(args)
+    other_tools_hf.print_exp_info(args)
+
+    # return one intance of trainer
+    trainer = BaseTrainer(args, ap = audio_path)
+    other_tools.load_checkpoints(trainer.model, args.test_ckpt, args.g_name)
+    
+    result = trainer.test_demo(999)
+    return result
+
+examples = [
+    ["demo/examples/2_scott_0_1_1.wav"],
+    ["demo/examples/2_scott_0_2_2.wav"],
+    ["demo/examples/2_scott_0_3_3.wav"],
+    ["demo/examples/2_scott_0_4_4.wav"],
+    ["demo/examples/2_scott_0_5_5.wav"],
+]
+
+demo = gr.Interface(
+    syntalker,  # function
+    inputs=[
+        # gr.File(label="Please upload SMPL-X file with npz format here.", file_types=["npz", "NPZ"]),
+        gr.Audio(),
+        gr.Radio(choices=["DDIM", "DDPM"], label="Please select a sample strategy", type="index", value="DDIM"),  # 0 for DDIM, 1 for DDPM
+        # gr.File(label="Please upload textgrid format file here.", file_types=["TextGrid", "Textgrid", "textgrid"])
+    ],  # input type
+    outputs=[
+        gr.Video(format="mp4", visible=True),
+        gr.File(label="download motion and visualize in blender")
+    ],
+    title='SynTalker: Enabling Synergistic Full-Body Control in Prompt-Based Co-Speech Motion Generation',
+    description="1. Upload your audio.  <br/>\
+        2. Then, sit back and wait for the rendering to happen! This may take a while (e.g. 2 minutes) <br/>\
+        3. After, you can view the videos.  <br/>\
+        4. Notice that we use a fix face animation, our method only produce body motion. <br/>\
+        5. Use DDPM sample strategy will generate a better result, while it will take more inference time.  \
+            ",
+    article="Project links: [SynTalker](https://robinwitch.github.io/SynTalker-Page). <br/>\
+             Reference links: [EMAGE](https://pantomatrix.github.io/EMAGE/). ", 
+    examples=examples,
+)
+
+            
+if __name__ == "__main__":
+    os.environ["MASTER_ADDR"]='127.0.0.1'
+    os.environ["MASTER_PORT"]='8675'
+    #os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"
+    demo.launch(share=True)

bash_raw_cospeech_download.sh

@@ -0,0 +1,4 @@
+mkdir -p datasets/BEAT_SMPL
+cd datasets/BEAT_SMPL
+gdown https://drive.google.com/uc?id=1_iXr0XiT_EdslXe4b0HwDr2OoOCrtlrB
+unzip beat_v2.0.0.zip

ckpt/beatx2_cospeech_diffusion/0403_212319_diffusion_rvqvae_128.yaml

@@ -0,0 +1,54 @@
+{a_encoder: null, a_fix_pre: false, a_pre_encoder: null, acc: 1, acc_weight: 0.0,
+  additional_data: false, adv_weight: 20.0, ali_weight: 0.0, amsgrad: false, apex: false,
+  asmr: 0.0, atcont: 0.0, atmr: 0.0, aud_prob: 1.0, audio_dims: 1, audio_f: 256, audio_fps: 16000,
+  audio_norm: false, audio_rep: onset+amplitude, audio_sr: 16000, batch_size: 40,
+  beat_align: true, benchmark: true, cache_only: false, cache_path: datasets/beat_cache/beat_smplx_en_emage_2_128/,
+  cf: 0.0, ch: 1.0, cl: 1.0, clean_final_seconds: 0, clean_first_seconds: 0, commit: 0.02,
+  config: configs/diffusion_rvqvae_128.yaml, csv_name: a2g_0, cu: 1.0, cudnn_enabled: true,
+  d_lr_weight: 0.2, d_name: null, data_path: /mnt/fu09a/chenbohong/PantoMatrix/scripts/EMAGE_2024/datasets/BEAT_SMPL/beat_v2.0.0/beat_english_v2.0.0/,
+  data_path_1: /mnt/fu09a/chenbohong/PantoMatrix/scripts/EMAGE_2024/datasets/hub/,
+  dataset: beat_sep_lower, ddp: false, debug: false, decay_epochs: 200, decay_rate: 0.1,
+  decode_fusion: null, depth: 3, deterministic: true, dilation_growth_rate: 3, disable_filtering: false,
+  div_reg_weight: 0.0, downs_t: [3], dropout_prob: 0.3, e_name: VAESKConv, e_path: weights/AESKConv_240_100.bin,
+  emb_width: 512, emo_rep: null, emotion_dims: 8, emotion_f: 0, epoch_stage: 0, epochs: 1000,
+  eval_model: motion_representation, f_encoder: 'null', f_fix_pre: false, f_pre_encoder: 'null',
+  fac_prob: 1.0, facial_dims: 100, facial_f: 0, facial_fps: 15, facial_norm: false,
+  facial_rep: smplxflame_30, fid_weight: 0.0, finger_net: original, freeze_wordembed: false,
+  fsmr: 0.0, ftmr: 0.0, fusion_mode: sum, g_name: MDM, gap_weight: 0.0, gpus: [0],
+  grad_norm: 0.99, hidden_size: 768, hvqvae_multipliers: [1], id_rep: onehot, input_context: both,
+  is_train: true, ita_weight: 0.0, iwa_weight: 0.0, joint_channel: 3, kld_aud_weight: 0.0,
+  kld_fac_weight: 0.0, kld_weight: 0.0, l: 4, l_bins: 512, l_mu: 0.99, levels: 1,
+  lf: 3.0, lh: 3.0, ll: 3.0, loader_workers: 0, log_period: 10, loss_contrastive_neg_weight: 0.005,
+  loss_contrastive_pos_weight: 0.2, loss_gan_weight: 5.0, loss_kld_weight: 0.1, loss_physical_weight: 0.0,
+  loss_reg_weight: 0.05, loss_regression_weight: 70.0, lr_base: 5.0e-05, lr_min: 1.0e-07,
+  lr_policy: step, lu: 3.0, m_conv: 1.0, m_decoder: null, m_encoder: 'null', m_fix_pre: false,
+  m_pre_encoder: 'null', mean_pose_path: /mnt/fu09a/chenbohong/PantoMatrix/beatx_2_330_mean.npy,
+  mean_trans_path: /mnt/fu09a/chenbohong/PantoMatrix/beatx_2_trans_mean.npy, model: denoiser,
+  momentum: 0.8, motion_f: 256, msmr: 0.0, mtmr: 0.0, multi_length_training: [1.0],
+  n_layer: 1, n_poses: 34, n_pre_poses: 4, name: 0403_212319_diffusion_rvqvae_128,
+  nesterov: true, new_cache: false, no_adv_epoch: 999, notes: '', opt: adam, opt_betas: [
+    0.5, 0.999], ori_joints: beat_smplx_joints, out_path: /mnt/fu09a/chenbohong/PantoMatrix/scripts/EMAGE_2024/outputs/audio2pose/,
+  pos_encoding_type: sin, pos_prob: 1.0, pose_dims: 330, pose_fps: 30, pose_length: 128,
+  pose_norm: true, pose_rep: smplxflame_30, pre_frames: 4, pre_type: zero, pretrain: false,
+  project: s2g, queue_size: 1024, random_seed: 2021, rec_aud_weight: 0.0, rec_fac_weight: 0.0,
+  rec_pos_weight: 0.0, rec_txt_weight: 0.0, rec_ver_weight: 0.0, rec_weight: 1.0,
+  root_path: /mnt/fu09a/chenbohong/PantoMatrix/scripts/EMAGE_2024/, root_weight: 1.0,
+  rot6d: true, sample_length: 34, sem_rep: null, sparse: 1, speaker_dims: 4, speaker_f: 0,
+  speaker_id: onehot, stat: ts, std_pose_path: /mnt/fu09a/chenbohong/PantoMatrix/beatx_2_330_std.npy,
+  std_trans_path: /mnt/fu09a/chenbohong/PantoMatrix/beatx_2_trans_std.npy, stride: 20,
+  strides_t: [2], t_encoder: 'null', t_fix_pre: false, t_pre_encoder: fasttext, tar_joints: beat_smplx_full,
+  test_ckpt: /mnt/fu09a/chenbohong/PantoMatrix/scripts/EMAGE_2024/outputs/audio2pose/custom/0330_140056_diffusion_rvqvae/last_300.bin,
+  test_data_path: /datasets/trinity/test/, test_length: 128, test_period: 20, train_data_path: /datasets/trinity/train/,
+  train_trans: true, trainer: diffusion_rvqvae, training_speakers: [2], tsmr: 0.0,
+  ttmr: 0.0, txt_prob: 1.0, use_amass: false, use_aug: false, use_bottleneck: true,
+  use_trans: true, vae_codebook_size: 256, vae_grow: [1, 1, 2, 1], vae_layer: 4, vae_length: 240,
+  vae_quantizer_lambda: 1.0, vae_test_dim: 330, vae_test_len: 32, vae_test_stride: 20,
+  val_data_path: /datasets/trinity/val/, variational: false, vel: 1, vel_weight: 0.0,
+  vqvae_ckpt: null, vqvae_hands_path: /mnt/fu09a/chenbohong/gdc/T2M-GPT/output_beatx2/RVQVAE_hands/net_300000.pth,
+  vqvae_latent_scale: 5.0, vqvae_lower_path: /mnt/fu09a/chenbohong/gdc/T2M-GPT/output_beatx2/RVQVAE_lower/net_300000.pth,
+  vqvae_lower_trans_path: /mnt/fu09a/chenbohong/gdc/T2M-GPT/output_beatx2/RVQVAE_lower_trans/net_300000.pth,
+  vqvae_reverse_decoder_dilation: true, vqvae_squeeze_scale: 4, vqvae_type: rvqvae,
+  vqvae_upper_path: /mnt/fu09a/chenbohong/gdc/T2M-GPT/output_beatx2/RVQVAE_upper/net_300000.pth,
+  warmup_epochs: 0, warmup_lr: 0.0005, wei_weight: 0.0, weight_decay: 0.0, width: 512,
+  word_cache: false, word_dims: 300, word_f: 256, word_index_num: 11195, word_rep: textgrid,
+  z_type: speaker}

ckpt/beatx2_cospeech_diffusion/1001_203942_diffusion_rvqvae_128_gaps-210-0.txt

@@ -0,0 +1,451 @@
+ 10-01 20:39:43 | {'a_encoder': None,
+ 'a_fix_pre': False,
+ 'a_pre_encoder': None,
+ 'acc': 1,
+ 'acc_weight': 0.0,
+ 'additional_data': False,
+ 'adv_weight': 20.0,
+ 'ali_weight': 0.0,
+ 'amsgrad': False,
+ 'apex': False,
+ 'asmr': 0.0,
+ 'atcont': 0.0,
+ 'atmr': 0.0,
+ 'aud_prob': 1.0,
+ 'audio_dims': 1,
+ 'audio_f': 256,
+ 'audio_fps': 16000,
+ 'audio_norm': False,
+ 'audio_rep': 'onset+amplitude',
+ 'audio_sr': 16000,
+ 'batch_size': 40,
+ 'beat_align': True,
+ 'benchmark': True,
+ 'cache_only': False,
+ 'cache_path': 'datasets/beat_cache/beat_smplx_en_emage_2_128/',
+ 'cf': 0.0,
+ 'ch': 1.0,
+ 'cl': 1.0,
+ 'clean_final_seconds': 0,
+ 'clean_first_seconds': 0,
+ 'commit': 0.02,
+ 'config': 'configs/diffusion_rvqvae_128_gaps-210-0.yaml',
+ 'csv_name': 'a2g_0',
+ 'cu': 1.0,
+ 'cudnn_enabled': True,
+ 'd_lr_weight': 0.2,
+ 'd_name': None,
+ 'data_path': './datasets/BEAT_SMPL/beat_v2.0.0/beat_english_v2.0.0/',
+ 'data_path_1': './datasets/hub/',
+ 'dataset': 'beat_sep_lower',
+ 'ddp': False,
+ 'debug': False,
+ 'decay_epochs': 500,
+ 'decay_rate': 0.1,
+ 'decode_fusion': None,
+ 'depth': 3,
+ 'deterministic': True,
+ 'dilation_growth_rate': 3,
+ 'disable_filtering': False,
+ 'div_reg_weight': 0.0,
+ 'downs_t': [3],
+ 'dropout_prob': 0.3,
+ 'e_name': 'VAESKConv',
+ 'e_path': 'weights/AESKConv_240_100.bin',
+ 'emb_width': 512,
+ 'emo_rep': None,
+ 'emotion_dims': 8,
+ 'emotion_f': 0,
+ 'epoch_stage': 0,
+ 'epochs': 2000,
+ 'eval_model': 'motion_representation',
+ 'f_encoder': 'null',
+ 'f_fix_pre': False,
+ 'f_pre_encoder': 'null',
+ 'fac_prob': 1.0,
+ 'facial_dims': 100,
+ 'facial_f': 0,
+ 'facial_fps': 15,
+ 'facial_norm': False,
+ 'facial_rep': 'smplxflame_30',
+ 'fid_weight': 0.0,
+ 'finger_net': 'original',
+ 'freeze_wordembed': False,
+ 'fsmr': 0.0,
+ 'ftmr': 0.0,
+ 'fusion_mode': 'sum',
+ 'g_name': 'MDM',
+ 'gap_weight': 0.0,
+ 'gpus': [0],
+ 'grad_norm': 0.99,
+ 'hidden_size': 768,
+ 'hvqvae_multipliers': [1],
+ 'id_rep': 'onehot',
+ 'input_context': 'both',
+ 'is_train': True,
+ 'ita_weight': 0.0,
+ 'iwa_weight': 0.0,
+ 'joint_channel': 3,
+ 'kld_aud_weight': 0.0,
+ 'kld_fac_weight': 0.0,
+ 'kld_weight': 0.0,
+ 'l': 4,
+ 'l_bins': 512,
+ 'l_mu': 0.99,
+ 'levels': 1,
+ 'lf': 3.0,
+ 'lh': 3.0,
+ 'll': 3.0,
+ 'loader_workers': 0,
+ 'log_period': 10,
+ 'loss_contrastive_neg_weight': 0.005,
+ 'loss_contrastive_pos_weight': 0.2,
+ 'loss_gan_weight': 5.0,
+ 'loss_kld_weight': 0.1,
+ 'loss_physical_weight': 0.0,
+ 'loss_reg_weight': 0.05,
+ 'loss_regression_weight': 70.0,
+ 'lr_base': 5e-05,
+ 'lr_min': 1e-07,
+ 'lr_policy': 'step',
+ 'lu': 3.0,
+ 'm_conv': 1.0,
+ 'm_decoder': None,
+ 'm_encoder': 'null',
+ 'm_fix_pre': False,
+ 'm_pre_encoder': 'null',
+ 'mean_pose_path': '../../beatx_2_330_mean.npy',
+ 'mean_trans_path': '../../beatx_2_trans_mean.npy',
+ 'model': 'denoiser',
+ 'momentum': 0.8,
+ 'motion_f': 256,
+ 'msmr': 0.0,
+ 'mtmr': 0.0,
+ 'multi_length_training': [1.0],
+ 'n_layer': 1,
+ 'n_poses': 34,
+ 'n_pre_poses': 4,
+ 'name': '1001_203942_diffusion_rvqvae_128_gaps-210-0',
+ 'nesterov': True,
+ 'new_cache': False,
+ 'no_adv_epoch': 999,
+ 'notes': '',
+ 'opt': 'adam',
+ 'opt_betas': [0.5, 0.999],
+ 'ori_joints': 'beat_smplx_joints',
+ 'out_path': './outputs/audio2pose/',
+ 'pos_encoding_type': 'sin',
+ 'pos_prob': 1.0,
+ 'pose_dims': 330,
+ 'pose_fps': 30,
+ 'pose_length': 128,
+ 'pose_norm': True,
+ 'pose_rep': 'smplxflame_30',
+ 'pre_frames': 4,
+ 'pre_type': 'zero',
+ 'pretrain': False,
+ 'project': 's2g',
+ 'queue_size': 1024,
+ 'random_seed': 2021,
+ 'rec_aud_weight': 0.0,
+ 'rec_fac_weight': 0.0,
+ 'rec_pos_weight': 0.0,
+ 'rec_txt_weight': 0.0,
+ 'rec_ver_weight': 0.0,
+ 'rec_weight': 1.0,
+ 'root_path': './',
+ 'root_weight': 1.0,
+ 'rot6d': True,
+ 'sample_length': 34,
+ 'sem_rep': None,
+ 'sparse': 1,
+ 'speaker_dims': 4,
+ 'speaker_f': 0,
+ 'speaker_id': 'onehot',
+ 'stat': 'ts',
+ 'std_pose_path': '../../beatx_2_330_std.npy',
+ 'std_trans_path': '../../beatx_2_trans_std.npy',
+ 'stride': 20,
+ 'strides_t': [2],
+ 't_encoder': 'null',
+ 't_fix_pre': False,
+ 't_pre_encoder': 'fasttext',
+ 'tar_joints': 'beat_smplx_full',
+ 'test_ckpt': './outputs/audio2pose/custom/0403_212319_diffusion_rvqvae_128/last_500.bin',
+ 'test_data_path': '/datasets/trinity/test/',
+ 'test_length': 128,
+ 'test_period': 20,
+ 'train_data_path': '/datasets/trinity/train/',
+ 'train_trans': True,
+ 'trainer': 'diffusion_rvqvae',
+ 'training_speakers': [2],
+ 'tsmr': 0.0,
+ 'ttmr': 0.0,
+ 'txt_prob': 1.0,
+ 'use_amass': False,
+ 'use_aug': False,
+ 'use_bottleneck': True,
+ 'use_motionclip': False,
+ 'use_trans': True,
+ 'vae_codebook_size': 256,
+ 'vae_grow': [1, 1, 2, 1],
+ 'vae_layer': 4,
+ 'vae_length': 240,
+ 'vae_quantizer_lambda': 1.0,
+ 'vae_test_dim': 330,
+ 'vae_test_len': 32,
+ 'vae_test_stride': 20,
+ 'val_data_path': '/datasets/trinity/val/',
+ 'variational': False,
+ 'vel': 1,
+ 'vel_weight': 0.0,
+ 'vqvae_ckpt': None,
+ 'vqvae_hands_path': './datasets/hub/output_beatx2/RVQVAE_hands/net_300000.pth',
+ 'vqvae_latent_scale': 5.0,
+ 'vqvae_lower_path': './datasets/hub/output_beatx2/RVQVAE_lower/net_300000.pth',
+ 'vqvae_lower_trans_path': './datasets/hub/output_beatx2/RVQVAE_lower_trans/net_300000.pth',
+ 'vqvae_reverse_decoder_dilation': True,
+ 'vqvae_squeeze_scale': 4,
+ 'vqvae_type': 'rvqvae',
+ 'vqvae_upper_path': './datasets/hub/output_beatx2/RVQVAE_upper/net_300000.pth',
+ 'warmup_epochs': 0,
+ 'warmup_lr': 0.0005,
+ 'wei_weight': 0.0,
+ 'weight_decay': 0.0,
+ 'width': 512,
+ 'word_cache': False,
+ 'word_dims': 300,
+ 'word_f': 256,
+ 'word_index_num': 11195,
+ 'word_rep': 'textgrid',
+ 'z_type': 'speaker'}
+ 10-01 20:39:43 | # ------------ 1001_203942_diffusion_rvqvae_128_gaps-210-0 ----------- #
+ 10-01 20:39:43 | PyTorch version: 2.4.1+cu121
+ 10-01 20:39:43 | CUDA version: 12.1
+ 10-01 20:39:43 | 1 GPUs
+ 10-01 20:39:43 | Random Seed: 2021
+ 10-01 20:39:46 | Audio bit rate: 16000
+ 10-01 20:39:46 | Reading data './datasets/BEAT_SMPL/beat_v2.0.0/beat_english_v2.0.0/'...
+ 10-01 20:39:46 | Creating the dataset cache...
+ 10-01 20:39:46 | Found the cache ./datasets/beat_cache/beat_smplx_en_emage_2_128/train/smplxflame_30_cache
+ 10-01 20:39:46 | Init train dataloader success
+ 10-01 20:39:46 | Init val dataloader success
+ 10-01 20:39:46 | Audio bit rate: 16000
+ 10-01 20:39:46 | Reading data './datasets/BEAT_SMPL/beat_v2.0.0/beat_english_v2.0.0/'...
+ 10-01 20:39:46 | Creating the dataset cache...
+ 10-01 20:39:46 | Found the cache ./datasets/beat_cache/beat_smplx_en_emage_2_128/test/smplxflame_30_cache
+ 10-01 20:39:46 | Init test dataloader success
+ 10-01 20:39:46 | DataParallel(
+  (module): MDM(
+    (WavEncoder): WavEncoder(
+      (feat_extractor): Sequential(
+        (0): BasicBlock(
+          (conv1): Conv1d(2, 64, kernel_size=(15,), stride=(5,), padding=(1700,))
+          (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (act1): LeakyReLU(negative_slope=0.01, inplace=True)
+          (conv2): Conv1d(64, 64, kernel_size=(15,), stride=(1,), padding=(7,))
+          (bn2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (act2): LeakyReLU(negative_slope=0.01, inplace=True)
+          (downsample): Sequential(
+            (0): Conv1d(2, 64, kernel_size=(15,), stride=(5,), padding=(1700,))
+            (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+        )
+        (1): BasicBlock(
+          (conv1): Conv1d(64, 64, kernel_size=(15,), stride=(6,))
+          (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (act1): LeakyReLU(negative_slope=0.01, inplace=True)
+          (conv2): Conv1d(64, 64, kernel_size=(15,), stride=(1,), padding=(7,))
+          (bn2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (act2): LeakyReLU(negative_slope=0.01, inplace=True)
+          (downsample): Sequential(
+            (0): Conv1d(64, 64, kernel_size=(15,), stride=(6,))
+            (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+        )
+        (2): BasicBlock(
+          (conv1): Conv1d(64, 64, kernel_size=(15,), stride=(1,), padding=(7,))
+          (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (act1): LeakyReLU(negative_slope=0.01, inplace=True)
+          (conv2): Conv1d(64, 64, kernel_size=(15,), stride=(1,), padding=(7,))
+          (bn2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (act2): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+        (3): BasicBlock(
+          (conv1): Conv1d(64, 128, kernel_size=(15,), stride=(6,))
+          (bn1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (act1): LeakyReLU(negative_slope=0.01, inplace=True)
+          (conv2): Conv1d(128, 128, kernel_size=(15,), stride=(1,), padding=(7,))
+          (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (act2): LeakyReLU(negative_slope=0.01, inplace=True)
+          (downsample): Sequential(
+            (0): Conv1d(64, 128, kernel_size=(15,), stride=(6,))
+            (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+        )
+        (4): BasicBlock(
+          (conv1): Conv1d(128, 128, kernel_size=(15,), stride=(1,), padding=(7,))
+          (bn1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (act1): LeakyReLU(negative_slope=0.01, inplace=True)
+          (conv2): Conv1d(128, 128, kernel_size=(15,), stride=(1,), padding=(7,))
+          (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (act2): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+        (5): BasicBlock(
+          (conv1): Conv1d(128, 256, kernel_size=(15,), stride=(3,))
+          (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (act1): LeakyReLU(negative_slope=0.01, inplace=True)
+          (conv2): Conv1d(256, 256, kernel_size=(15,), stride=(1,), padding=(7,))
+          (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (act2): LeakyReLU(negative_slope=0.01, inplace=True)
+          (downsample): Sequential(
+            (0): Conv1d(128, 256, kernel_size=(15,), stride=(3,))
+            (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+        )
+      )
+    )
+    (text_encoder_body): Linear(in_features=300, out_features=256, bias=True)
+    (text_pre_encoder_body): Embedding(11195, 300)
+    (sequence_pos_encoder): PositionalEncoding(
+      (dropout): Dropout(p=0.1, inplace=False)
+    )
+    (mytimmblocks): ModuleList(
+      (0-7): 8 x Block(
+        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
+        (attn): Attention(
+          (qkv): Linear(in_features=512, out_features=1536, bias=False)
+          (q_norm): Identity()
+          (k_norm): Identity()
+          (attn_drop): Dropout(p=0.0, inplace=False)
+          (proj): Linear(in_features=512, out_features=512, bias=True)
+          (proj_drop): Dropout(p=0.0, inplace=False)
+        )
+        (ls1): Identity()
+        (drop_path1): DropPath(drop_prob=0.100)
+        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
+        (mlp): Mlp(
+          (fc1): Linear(in_features=512, out_features=1024, bias=True)
+          (act): GELU(approximate='none')
+          (drop1): Dropout(p=0.0, inplace=False)
+          (norm): Identity()
+          (fc2): Linear(in_features=1024, out_features=512, bias=True)
+          (drop2): Dropout(p=0.0, inplace=False)
+        )
+        (ls2): Identity()
+        (drop_path2): DropPath(drop_prob=0.100)
+      )
+    )
+    (embed_timestep): TimestepEmbedder(
+      (sequence_pos_encoder): PositionalEncoding(
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (time_embed): Sequential(
+        (0): Linear(in_features=512, out_features=512, bias=True)
+        (1): SiLU()
+        (2): Linear(in_features=512, out_features=512, bias=True)
+      )
+    )
+    (embed_style): Linear(in_features=6, out_features=64, bias=True)
+    (embed_text): Linear(in_features=6144, out_features=512, bias=True)
+    (output_process): OutputProcess(
+      (poseFinal): Linear(in_features=512, out_features=1536, bias=True)
+    )
+    (rel_pos): SinusoidalEmbeddings()
+    (input_process): InputProcess(
+      (poseEmbedding): Linear(in_features=1536, out_features=512, bias=True)
+    )
+    (input_process2): Linear(in_features=1280, out_features=512, bias=True)
+    (mix_audio_text): Linear(in_features=512, out_features=256, bias=True)
+  )
+)
+ 10-01 20:39:46 | init MDM success
+ 10-01 20:39:46 | load self-pretrained checkpoints for VAESKConv
+ 10-01 20:39:46 | load self-pretrained checkpoints for VAESKConv
+ 10-01 20:39:46 | VAESKConv(
+  (encoder): LocalEncoder(
+    (layers): ModuleList(
+      (0): Sequential(
+        (0): SkeletonResidual(
+          (residual): Sequential(
+            (0): SkeletonConv()
+            (1): GroupNorm(10, 330, eps=1e-05, affine=True)
+          )
+          (shortcut): SkeletonConv()
+          (common): Sequential(
+            (0): SkeletonPool()
+            (1): Tanh()
+          )
+        )
+      )
+      (1): Sequential(
+        (0): SkeletonResidual(
+          (residual): Sequential(
+            (0): SkeletonConv()
+            (1): GroupNorm(10, 210, eps=1e-05, affine=True)
+          )
+          (shortcut): SkeletonConv()
+          (common): Sequential(
+            (0): SkeletonPool()
+            (1): Tanh()
+          )
+        )
+      )
+      (2-3): 2 x Sequential(
+        (0): SkeletonResidual(
+          (residual): Sequential(
+            (0): SkeletonConv()
+            (1): GroupNorm(10, 240, eps=1e-05, affine=True)
+          )
+          (shortcut): SkeletonConv()
+          (common): Sequential(
+            (0): Tanh()
+          )
+        )
+      )
+    )
+  )
+  (decoder): VQDecoderV3(
+    (main): Sequential(
+      (0): ResBlock(
+        (model): Sequential(
+          (0): Conv1d(240, 240, kernel_size=(3,), stride=(1,), padding=(1,))
+          (1): LeakyReLU(negative_slope=0.2, inplace=True)
+          (2): Conv1d(240, 240, kernel_size=(3,), stride=(1,), padding=(1,))
+        )
+      )
+      (1): ResBlock(
+        (model): Sequential(
+          (0): Conv1d(240, 240, kernel_size=(3,), stride=(1,), padding=(1,))
+          (1): LeakyReLU(negative_slope=0.2, inplace=True)
+          (2): Conv1d(240, 240, kernel_size=(3,), stride=(1,), padding=(1,))
+        )
+      )
+      (2): Upsample(scale_factor=2.0, mode='nearest')
+      (3): Conv1d(240, 240, kernel_size=(3,), stride=(1,), padding=(1,))
+      (4): LeakyReLU(negative_slope=0.2, inplace=True)
+      (5): Upsample(scale_factor=2.0, mode='nearest')
+      (6): Conv1d(240, 240, kernel_size=(3,), stride=(1,), padding=(1,))
+      (7): LeakyReLU(negative_slope=0.2, inplace=True)
+      (8): Upsample(scale_factor=2.0, mode='nearest')
+      (9): Conv1d(240, 240, kernel_size=(3,), stride=(1,), padding=(1,))
+      (10): LeakyReLU(negative_slope=0.2, inplace=True)
+      (11): Upsample(scale_factor=2.0, mode='nearest')
+      (12): Conv1d(240, 330, kernel_size=(3,), stride=(1,), padding=(1,))
+      (13): LeakyReLU(negative_slope=0.2, inplace=True)
+      (14): Conv1d(330, 330, kernel_size=(3,), stride=(1,), padding=(1,))
+    )
+  )
+  (fc_mu): Linear(in_features=240, out_features=240, bias=True)
+  (fc_logvar): Linear(in_features=240, out_features=240, bias=True)
+)
+ 10-01 20:39:46 | init VAESKConv success
+ 10-01 20:39:47 | load self-pretrained checkpoints for VAESKConv
+ 10-01 20:39:47 | load self-pretrained checkpoints for VAESKConv
+ 10-01 20:39:48 | load self-pretrained checkpoints for MDM
+ 10-01 21:08:57 | l2 loss: 0.0
+ 10-01 21:08:57 | lvel loss: 0.0
+ 10-01 21:08:58 | fid score: 0.46525881529758983
+ 10-01 21:08:58 | align score: 0.7361291368819373
+ 10-01 21:08:58 | l1div score: 12.30848217010498
+ 10-01 21:08:58 | total inference time: 1749 s for 945 s motion

ckpt/beatx2_cospeech_diffusion/last_500.bin

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ckpt/beatx2_rvqvae/RVQVAE_hands/net_300000.pth

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+size 83048747

ckpt/beatx2_rvqvae/RVQVAE_lower/net_300000.pth

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+version https://git-lfs.github.com/spec/v1
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+size 81499947

ckpt/beatx2_rvqvae/RVQVAE_lower_trans/net_300000.pth

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+size 81536811

ckpt/beatx2_rvqvae/RVQVAE_upper/net_300000.pth

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+size 81794923

configs/beat2_rvqvae.yaml

@@ -0,0 +1,134 @@
+is_train: True
+ddp: False
+stat: ts
+root_path: ./
+out_path: ./outputs/audio2pose/
+project: s2g
+data_path: ./datasets/BEAT_SMPL/beat_v2.0.0/beat_english_v2.0.0/
+e_path:  weights/AESKConv_240_100.bin
+eval_model: motion_representation
+e_name: VAESKConv
+test_ckpt: ./outputs/audio2pose/custom/0112_001634_emage/last_200.bin
+data_path_1: ./datasets/hub/
+
+vae_test_len: 32
+vae_test_dim: 330
+vae_test_stride: 20
+vae_length: 240
+vae_codebook_size: 256
+vae_layer: 4
+vae_grow: [1,1,2,1]
+variational: False
+
+# data config
+training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] #[2]
+additional_data: False
+cache_path: datasets/beat_cache/beat_smplx_en_emage_2_rvqvae/
+dataset: mix_sep
+new_cache: True
+use_amass: False
+# motion config
+ori_joints: beat_smplx_joints
+tar_joints: beat_smplx_full
+pose_rep: smplxflame_30
+pose_norm: False
+pose_fps: 30
+rot6d: True
+pre_frames: 4
+pose_dims: 330
+pose_length: 64
+stride: 20
+test_length: 64
+motion_f: 256
+m_pre_encoder: null
+m_encoder: null
+m_fix_pre: False
+
+# audio config
+audio_rep: onset+amplitude
+audio_sr: 16000
+audio_fps: 16000
+audio_norm: False
+audio_f: 256
+# a_pre_encoder: tcn_camn
+# a_encoder: none
+# a_fix_pre: False
+
+# text config
+word_rep: textgrid
+word_index_num: 11195
+word_dims: 300
+freeze_wordembed: False
+word_f: 256
+t_pre_encoder: fasttext
+t_encoder: null
+t_fix_pre: False
+
+# facial config
+facial_rep: smplxflame_30
+facial_dims: 100
+facial_norm: False
+facial_f: 0
+f_pre_encoder: null
+f_encoder: null
+f_fix_pre: False
+
+# speaker config
+id_rep: onehot
+speaker_f: 0
+
+# model config
+batch_size: 80  #80
+# warmup_epochs: 1
+# warmup_lr: 1e-6
+lr_base: 4e-4
+model: motion_representation
+g_name: VQVAEConvZero
+trainer: ae_total
+hidden_size: 768
+n_layer: 1
+ 
+rec_weight: 1
+grad_norm: 0.99
+epochs: 200
+test_period: 20
+ll: 3
+lf: 3
+lu: 3
+lh: 3
+cl: 1
+cf: 0
+cu: 1
+ch: 1
+
+
+
+#below is vavae config, copy from QPGESTURE
+#Codebook Configs
+levels: 1
+downs_t: [3]
+strides_t : [2]
+emb_width : 512
+l_bins : 512
+l_mu : 0.99
+commit : 0.1
+hvqvae_multipliers : [1]
+width: 512
+depth: 3
+m_conv : 1.0
+dilation_growth_rate : 3
+sample_length: 80
+use_bottleneck: True
+joint_channel: 6
+# depth: 3
+# width: 128
+# m_conv: 1.0
+# dilation_growth_rate: 1
+# dilation_cycle: None
+vel: 1      # 1 -> 0
+acc: 1      # 1 -> 0
+vqvae_reverse_decoder_dilation: True
+
+
+## below is special for emage
+rec_pos_weight : 1.0

configs/diffusion_rvqvae_128.yaml

@@ -0,0 +1,118 @@
+is_train: True
+ddp: False
+stat: ts
+root_path: ./
+out_path: ./outputs/audio2pose/
+project: s2g
+data_path: ./datasets/BEAT_SMPL/beat_v2.0.0/beat_english_v2.0.0/
+e_path:  weights/AESKConv_240_100.bin
+eval_model: motion_representation
+e_name: VAESKConv
+test_ckpt: ./ckpt/beatx2_cospeech_diffusion/last_500.bin
+data_path_1: ./datasets/hub/
+pose_norm: True
+
+
+mean_pose_path: ./mean_std/beatx_2_330_mean.npy
+std_pose_path: ./mean_std/beatx_2_330_std.npy
+
+mean_trans_path: ./mean_std/beatx_2_trans_mean.npy
+std_trans_path: ./mean_std/beatx_2_trans_std.npy
+
+
+vqvae_upper_path: ./ckpt/beatx2_rvqvae/RVQVAE_upper/net_300000.pth
+vqvae_hands_path: ./ckpt/beatx2_rvqvae/RVQVAE_hands/net_300000.pth
+vqvae_lower_path: ./ckpt/beatx2_rvqvae/RVQVAE_lower/net_300000.pth
+
+vqvae_lower_trans_path: ./ckpt/beatx2_rvqvae/RVQVAE_lower_trans/net_300000.pth
+use_trans: True
+
+decay_epoch: 500
+
+vqvae_squeeze_scale: 4 
+vqvae_type: rvqvae
+vqvae_latent_scale: 5
+
+vae_test_len: 32
+vae_test_dim: 330
+vae_test_stride: 20
+vae_length: 240
+vae_codebook_size: 256
+vae_layer: 4
+vae_grow: [1,1,2,1]
+variational: False
+
+# data config
+training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
+additional_data: False
+cache_path: datasets/beat_cache/beat_smplx_en_emage_2_128/
+dataset: beat_sep_lower
+new_cache: False
+
+# motion config
+ori_joints: beat_smplx_joints
+tar_joints: beat_smplx_full
+pose_rep: smplxflame_30
+pose_fps: 30
+rot6d: True
+pre_frames: 4
+pose_dims: 330
+pose_length: 128
+stride: 20
+test_length: 128
+motion_f: 256
+m_pre_encoder: null
+m_encoder: null
+m_fix_pre: False
+
+
+audio_rep: onset+amplitude
+audio_sr: 16000
+audio_fps: 16000
+audio_norm: False
+audio_f: 256
+
+
+word_rep: textgrid
+word_index_num: 11195
+word_dims: 300
+freeze_wordembed: False
+word_f: 256
+t_pre_encoder: fasttext
+t_encoder: null
+t_fix_pre: False
+
+
+facial_rep: smplxflame_30
+facial_dims: 100
+facial_norm: False
+facial_f: 0
+f_pre_encoder: null
+f_encoder: null
+f_fix_pre: False
+
+
+id_rep: onehot
+speaker_f: 0
+
+
+batch_size: 40 
+lr_base: 5e-5
+model: denoiser
+g_name: MDM
+trainer: diffusion_rvqvae
+hidden_size: 768
+n_layer: 1
+ 
+rec_weight: 1
+grad_norm: 0.99
+epochs: 2000
+test_period: 20
+ll: 3
+lf: 3
+lu: 3
+lh: 3
+cl: 1
+cf: 0
+cu: 1
+ch: 1

configs/diffusion_rvqvae_128_hf.yaml

@@ -0,0 +1,118 @@
+is_train: True
+ddp: False
+stat: ts
+root_path: ./
+out_path: ./outputs/audio2pose/
+project: s2g
+data_path: ./datasets/BEAT_SMPL/beat_v2.0.0/beat_english_v2.0.0/
+e_path:  weights/AESKConv_240_100.bin
+eval_model: motion_representation
+e_name: VAESKConv
+test_ckpt: ./ckpt/beatx2_cospeech_diffusion/last_500.bin
+data_path_1: ./datasets/hub/
+pose_norm: True
+
+
+mean_pose_path: ./mean_std/beatx_2_330_mean.npy
+std_pose_path: ./mean_std/beatx_2_330_std.npy
+
+mean_trans_path: ./mean_std/beatx_2_trans_mean.npy
+std_trans_path: ./mean_std/beatx_2_trans_std.npy
+
+
+vqvae_upper_path: ./ckpt/beatx2_rvqvae/RVQVAE_upper/net_300000.pth
+vqvae_hands_path: ./ckpt/beatx2_rvqvae/RVQVAE_hands/net_300000.pth
+vqvae_lower_path: ./ckpt/beatx2_rvqvae/RVQVAE_lower/net_300000.pth
+
+vqvae_lower_trans_path: ./ckpt/beatx2_rvqvae/RVQVAE_lower_trans/net_300000.pth
+use_trans: True
+
+decay_epoch: 500
+
+vqvae_squeeze_scale: 4 
+vqvae_type: rvqvae
+vqvae_latent_scale: 5
+
+vae_test_len: 32
+vae_test_dim: 330
+vae_test_stride: 20
+vae_length: 240
+vae_codebook_size: 256
+vae_layer: 4
+vae_grow: [1,1,2,1]
+variational: False
+
+# data config
+training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
+additional_data: False
+cache_path: datasets/beat_cache/web_demo_test/
+dataset: beat_sep_lower_single
+new_cache: True
+
+# motion config
+ori_joints: beat_smplx_joints
+tar_joints: beat_smplx_full
+pose_rep: smplxflame_30
+pose_fps: 30
+rot6d: True
+pre_frames: 4
+pose_dims: 330
+pose_length: 128
+stride: 20
+test_length: 128
+motion_f: 256
+m_pre_encoder: null
+m_encoder: null
+m_fix_pre: False
+
+
+audio_rep: onset+amplitude
+audio_sr: 16000
+audio_fps: 16000
+audio_norm: False
+audio_f: 256
+
+
+word_rep: textgrid
+word_index_num: 11195
+word_dims: 300
+freeze_wordembed: False
+word_f: 256
+t_pre_encoder: fasttext
+t_encoder: null
+t_fix_pre: False
+
+
+facial_rep: smplxflame_30
+facial_dims: 100
+facial_norm: False
+facial_f: 0
+f_pre_encoder: null
+f_encoder: null
+f_fix_pre: False
+
+
+id_rep: onehot
+speaker_f: 0
+
+
+batch_size: 40 
+lr_base: 5e-5
+model: denoiser
+g_name: MDM
+trainer: diffusion_rvqvae
+hidden_size: 768
+n_layer: 1
+ 
+rec_weight: 1
+grad_norm: 0.99
+epochs: 2000
+test_period: 20
+ll: 3
+lf: 3
+lu: 3
+lh: 3
+cl: 1
+cf: 0
+cu: 1
+ch: 1

dataloaders/amass_sep_lower.py

@@ -0,0 +1,713 @@
+import os
+import pickle
+import math
+import shutil
+import numpy as np
+import lmdb as lmdb
+import textgrid as tg
+import pandas as pd
+import torch
+import glob
+import json
+from termcolor import colored
+from loguru import logger
+from collections import defaultdict
+from torch.utils.data import Dataset
+import torch.distributed as dist
+#import pyarrow
+import pickle
+import librosa
+import smplx
+import glob
+
+from .build_vocab import Vocab
+from .utils.audio_features import Wav2Vec2Model
+from .data_tools import joints_list
+from .utils import rotation_conversions as rc
+from .utils import other_tools
+
+# ACCAD 120
+# BioMotionLab_NTroje 120
+# CMU 很复杂
+# EKUT 100
+# Eyes_Japan_Dataset 很复杂
+# HumanEva 很复杂
+# KIT 100
+# MPI_HDM05 120
+# MPI_Limits 120
+# MPI_mosh 很复杂
+# SFU 120
+# SSM_synced 很复杂
+# TCD_handMocap 很复杂
+# TotalCapture 60
+# Transitions_mocap 120
+
+all_sequences = [
+    'ACCAD',
+    'BioMotionLab_NTroje',
+    'CMU',
+    'EKUT',
+    'Eyes_Japan_Dataset',
+    'HumanEva',
+    'KIT',
+    'MPI_HDM05',
+    'MPI_Limits',
+    'MPI_mosh',
+    'SFU',
+    'SSM_synced',
+    'TCD_handMocap',
+    'TotalCapture',
+    'Transitions_mocap',
+]
+amass_test_split = ['Transitions_mocap', 'SSM_synced']
+amass_vald_split = ['HumanEva', 'MPI_HDM05', 'SFU', 'MPI_mosh']
+amass_train_split = ['BioMotionLab_NTroje', 'Eyes_Japan_Dataset', 'TotalCapture', 'KIT', 'ACCAD', 'CMU', 'MPI_Limits',
+                     'TCD_handMocap', 'EKUT']
+
+# 上面这些spilt方式是MOTION CLIP的，但是由于motionx中的framerate处理有问题，我先暂且只挑部分数据集进行训练
+# 这些都是120fps的
+# amass_test_split = ['SFU']
+# amass_vald_split = ['MPI_Limits']
+# amass_train_split = ['BioMotionLab_NTroje', 'MPI_HDM05', 'ACCAD','Transitions_mocap']
+
+
+amass_splits = {
+    'test': amass_test_split,
+    'val': amass_vald_split,
+    'train': amass_train_split
+}
+class CustomDataset(Dataset):
+    def __init__(self, args, loader_type, augmentation=None, kwargs=None, build_cache=True):
+        self.args = args
+        self.loader_type = loader_type
+
+        self.rank = dist.get_rank()
+        self.ori_stride = self.args.stride
+        self.ori_length = self.args.pose_length
+        self.alignment = [0,0] # for trinity
+        
+        self.ori_joint_list = joints_list[self.args.ori_joints]
+        self.tar_joint_list = joints_list[self.args.tar_joints]
+        if 'smplx' in self.args.pose_rep:
+            self.joint_mask = np.zeros(len(list(self.ori_joint_list.keys()))*3)
+            self.joints = len(list(self.tar_joint_list.keys()))  
+            for joint_name in self.tar_joint_list:
+                self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        else:
+            self.joints = len(list(self.ori_joint_list.keys()))+1
+            self.joint_mask = np.zeros(self.joints*3)
+            for joint_name in self.tar_joint_list:
+                if joint_name == "Hips":
+                    self.joint_mask[3:6] = 1
+                else:
+                    self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        # select trainable joints
+        self.smplx = smplx.create(
+            self.args.data_path_1+"smplx_models/", 
+            model_type='smplx',
+            gender='NEUTRAL_2020', 
+            use_face_contour=False,
+            num_betas=300,
+            num_expression_coeffs=100, 
+            ext='npz',
+            use_pca=False,
+        ).cuda().eval()
+
+        split_rule = pd.read_csv(args.data_path+"train_test_split.csv")
+        self.selected_file = split_rule.loc[(split_rule['type'] == loader_type) & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+        if args.additional_data and loader_type == 'train':
+            split_b = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            #self.selected_file = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            self.selected_file = pd.concat([self.selected_file, split_b])
+        if self.selected_file.empty:
+            logger.warning(f"{loader_type} is empty for speaker {self.args.training_speakers}, use train set 0-8 instead")
+            self.selected_file = split_rule.loc[(split_rule['type'] == 'train') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            self.selected_file = self.selected_file.iloc[0:8]
+        self.data_dir = args.data_path 
+        
+        if loader_type == "test": 
+            self.args.multi_length_training = [1.0]
+        self.max_length = int(args.pose_length * self.args.multi_length_training[-1])
+        self.max_audio_pre_len = math.floor(args.pose_length / args.pose_fps * self.args.audio_sr)
+        if self.max_audio_pre_len > self.args.test_length*self.args.audio_sr: 
+            self.max_audio_pre_len = self.args.test_length*self.args.audio_sr
+        
+        if args.word_rep is not None:
+            with open(f"{args.data_path}weights/vocab.pkl", 'rb') as f:
+                self.lang_model = pickle.load(f)
+                
+        preloaded_dir = self.args.root_path + 'datasets/beat_cache/amass_smplx_en_emage_new/' + loader_type + f"/{args.pose_rep}_cache"      
+        # if args.pose_norm:
+        #     # careful for rotation vectors
+        #     if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy"):
+        #         self.calculate_mean_pose()
+        #     self.mean_pose = np.load(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy")
+        #     self.std_pose = np.load(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_std.npy")
+        # if args.audio_norm:
+        #     if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/bvh_mean.npy"):
+        #         self.calculate_mean_audio()
+        #     self.mean_audio = np.load(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/npy_mean.npy")
+        #     self.std_audio = np.load(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/npy_std.npy")
+        # if args.facial_norm:
+        #     if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy"):
+        #         self.calculate_mean_face()
+        #     self.mean_facial = np.load(args.data_path+args.mean_pose_path+f"{args.facial_rep}/json_mean.npy")
+        #     self.std_facial = np.load(args.data_path+args.mean_pose_path+f"{args.facial_rep}/json_std.npy")
+        if self.args.beat_align:
+            if not os.path.exists(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy"):
+                self.calculate_mean_velocity(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy")
+            self.avg_vel = np.load(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy")
+
+        if build_cache and self.rank == 0:
+            self.build_cache(preloaded_dir)
+        self.lmdb_env = lmdb.open(preloaded_dir, readonly=True, lock=False)
+        with self.lmdb_env.begin() as txn:
+            self.n_samples = txn.stat()["entries"] 
+
+    
+    def calculate_mean_velocity(self, save_path):
+        self.smplx = smplx.create(
+            self.args.data_path_1+"smplx_models/", 
+            model_type='smplx',
+            gender='NEUTRAL_2020', 
+            use_face_contour=False,
+            num_betas=300,
+            num_expression_coeffs=100, 
+            ext='npz',
+            use_pca=False,
+        ).cuda().eval()
+        dir_p = self.data_dir + self.args.pose_rep + "/"
+        all_list = []
+        from tqdm import tqdm
+        for tar in tqdm(os.listdir(dir_p)):
+            if tar.endswith(".npz"):
+                m_data = np.load(dir_p+tar, allow_pickle=True)
+                betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"]
+                n, c = poses.shape[0], poses.shape[1]
+                betas = betas.reshape(1, 300)
+                betas = np.tile(betas, (n, 1))
+                betas = torch.from_numpy(betas).cuda().float()
+                poses = torch.from_numpy(poses.reshape(n, c)).cuda().float()
+                exps = torch.from_numpy(exps.reshape(n, 100)).cuda().float()
+                trans = torch.from_numpy(trans.reshape(n, 3)).cuda().float()
+                max_length = 128
+                s, r = n//max_length, n%max_length
+                #print(n, s, r)
+                all_tensor = []
+                for i in range(s):
+                    with torch.no_grad():
+                        joints = self.smplx(
+                            betas=betas[i*max_length:(i+1)*max_length], 
+                            transl=trans[i*max_length:(i+1)*max_length], 
+                            expression=exps[i*max_length:(i+1)*max_length], 
+                            jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], 
+                            global_orient=poses[i*max_length:(i+1)*max_length,:3], 
+                            body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], 
+                            left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], 
+                            right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], 
+                            return_verts=True,
+                            return_joints=True,
+                            leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], 
+                            reye_pose=poses[i*max_length:(i+1)*max_length, 72:75],
+                        )['joints'][:, :55, :].reshape(max_length, 55*3)
+                    all_tensor.append(joints)
+                if r != 0:
+                    with torch.no_grad():
+                        joints = self.smplx(
+                            betas=betas[s*max_length:s*max_length+r], 
+                            transl=trans[s*max_length:s*max_length+r], 
+                            expression=exps[s*max_length:s*max_length+r], 
+                            jaw_pose=poses[s*max_length:s*max_length+r, 66:69], 
+                            global_orient=poses[s*max_length:s*max_length+r,:3], 
+                            body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], 
+                            left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], 
+                            right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], 
+                            return_verts=True,
+                            return_joints=True,
+                            leye_pose=poses[s*max_length:s*max_length+r, 69:72], 
+                            reye_pose=poses[s*max_length:s*max_length+r, 72:75],
+                        )['joints'][:, :55, :].reshape(r, 55*3)
+                    all_tensor.append(joints)
+                joints = torch.cat(all_tensor, axis=0)
+                joints = joints.permute(1, 0)
+                dt = 1/30
+            # first steps is forward diff (t+1 - t) / dt
+                init_vel = (joints[:, 1:2] - joints[:, :1]) / dt
+                # middle steps are second order (t+1 - t-1) / 2dt
+                middle_vel = (joints[:, 2:] - joints[:, 0:-2]) / (2 * dt)
+                # last step is backward diff (t - t-1) / dt
+                final_vel = (joints[:, -1:] - joints[:, -2:-1]) / dt
+                #print(joints.shape, init_vel.shape, middle_vel.shape, final_vel.shape)
+                vel_seq = torch.cat([init_vel, middle_vel, final_vel], dim=1).permute(1, 0).reshape(n, 55, 3)
+                #print(vel_seq.shape)
+                #.permute(1, 0).reshape(n, 55, 3)
+                vel_seq_np = vel_seq.cpu().numpy()
+                vel_joints_np = np.linalg.norm(vel_seq_np, axis=2) # n * 55
+                all_list.append(vel_joints_np)
+        avg_vel = np.mean(np.concatenate(all_list, axis=0),axis=0) # 55
+        np.save(save_path, avg_vel)
+        
+    
+    def build_cache(self, preloaded_dir):
+        logger.info(f"Audio bit rate: {self.args.audio_fps}")
+        logger.info("Reading data '{}'...".format(self.data_dir))
+        logger.info("Creating the dataset cache...")
+        if self.args.new_cache:
+            if os.path.exists(preloaded_dir):
+                shutil.rmtree(preloaded_dir)
+        if os.path.exists(preloaded_dir):
+            logger.info("Found the cache {}".format(preloaded_dir))
+        elif self.loader_type == "test":
+            self.cache_generation(
+                preloaded_dir, True, 
+                0, 0,
+                is_test=True)
+        else:
+            self.cache_generation(
+                preloaded_dir, self.args.disable_filtering, 
+                self.args.clean_first_seconds, self.args.clean_final_seconds,
+                is_test=False)
+        
+    def __len__(self):
+        return self.n_samples
+    
+    
+    def load_amass(self,data):
+        ## 这个是用来
+        # 修改amass数据里面的朝向，原本在blender里面是Z轴向上，目标是Y轴向上，当时面向目前没改
+        
+        data_dict = {key: data[key] for key in data}
+        frames = data_dict['poses'].shape[0]
+        b = data_dict['poses'][...,:3]
+        b = rc.axis_angle_to_matrix(torch.from_numpy(b))
+        rot_matrix = np.array([[1.0, 0.0, 0.0], [0.0 , 0.0, 1.0], [0.0, -1.0, 0.0]])
+        c = np.einsum('ij,kjl->kil',rot_matrix,b)
+        c = rc.matrix_to_axis_angle(torch.from_numpy(c))
+        data_dict['poses'][...,:3] = c
+        
+        trans_matrix1 = np.array([[1.0, 0.0, 0.0], [0.0 , 0.0, -1.0], [0.0, 1.0, 0.0]])
+        data_dict['trans'] = np.einsum("bi,ij->bj",data_dict['trans'],trans_matrix1)
+        
+        betas300 = np.zeros(300)
+        betas300[:16] = data_dict['betas']
+        data_dict['betas'] = betas300
+        data_dict["expressions"] = np.zeros((frames,100))
+        
+        return data_dict
+
+    def cache_generation(self, out_lmdb_dir, disable_filtering, clean_first_seconds,  clean_final_seconds, is_test=False):
+        # if "wav2vec2" in self.args.audio_rep:
+        #     self.wav2vec_model = Wav2Vec2Model.from_pretrained(f"{self.args.data_path_1}/hub/transformer/wav2vec2-base-960h")
+        #     self.wav2vec_model.feature_extractor._freeze_parameters()
+        #     self.wav2vec_model = self.wav2vec_model.cuda()
+        #     self.wav2vec_model.eval()
+        
+        self.n_out_samples = 0
+        # create db for samples
+        if not os.path.exists(out_lmdb_dir): os.makedirs(out_lmdb_dir)
+        dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 500))# 500G
+        n_filtered_out = defaultdict(int)
+    
+              
+        if self.args.use_amass:
+            amass_dir = '/mnt/fu09a/chenbohong/PantoMatrix/scripts/EMAGE_2024/datasets/AMASS_SMPLX'
+            for dataset in amass_splits[self.loader_type]:
+                search_path = os.path.join(amass_dir,dataset, '**', '*.npz')
+                npz_files = glob.glob(search_path, recursive=True)     
+                for index, file_name in enumerate(npz_files):
+                    f_name = file_name.split('/')[-1]
+                    ext = ".npz" if "smplx" in self.args.pose_rep else ".bvh"
+                    pose_file = file_name
+                    pose_each_file = []
+                    trans_each_file = []
+                    trans_v_each_file = []
+                    shape_each_file = []
+                    audio_each_file = []
+                    facial_each_file = []
+                    word_each_file = []
+                    emo_each_file = []
+                    sem_each_file = []
+                    vid_each_file = []
+                    id_pose = f_name #1_wayne_0_1_1
+                    get_foot_contact = True
+                    logger.info(colored(f"# ---- Building cache for Pose   {id_pose} ---- #", "blue"))
+                    if "smplx" in self.args.pose_rep:
+                        pose_data = np.load(pose_file, allow_pickle=True)
+                        if len(pose_data.files)==6:
+                            logger.info(colored(f"# ---- state file ---- #", "red"))
+                            continue
+                        assert 30%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 30'
+                        assert self.args.pose_fps == 30, "should 30"
+                        m_data = np.load(pose_file, allow_pickle=True)
+                        m_data= self.load_amass(m_data)
+                        betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"]
+                        mocap_framerate = float(m_data['mocap_frame_rate'])
+                        stride = round(mocap_framerate / self.args.pose_fps)
+                        pose_each_file = poses[::stride]
+                        trans_each_file = trans[::stride]
+                        trans_each_file[:,0] = trans_each_file[:,0] - trans_each_file[0,0]
+                        trans_each_file[:,2] = trans_each_file[:,2] - trans_each_file[0,2]
+                        trans_v_each_file = np.zeros_like(trans_each_file)
+                        trans_v_each_file[1:,0] = trans_each_file[1:,0] - trans_each_file[:-1,0]
+                        trans_v_each_file[0,0] = trans_v_each_file[1,0]
+                        trans_v_each_file[1:,2] = trans_each_file[1:,2] - trans_each_file[:-1,2]
+                        trans_v_each_file[0,2] = trans_v_each_file[1,2]
+                        trans_v_each_file[:,1] = trans_each_file[:,1]
+                        
+                        
+                        shape_each_file = np.repeat(betas.reshape(1, -1), pose_each_file.shape[0], axis=0)
+                        
+                        n, c = poses.shape[0], poses.shape[1]
+                        betas = betas.reshape(1, 300)
+                        betas = np.tile(betas, (n, 1))
+                        betas = torch.from_numpy(betas).cuda().float()
+                        poses = torch.from_numpy(poses.reshape(n, c)).cuda().float()
+                        exps = torch.from_numpy(exps.reshape(n, 100)).cuda().float()
+                        trans = torch.from_numpy(trans.reshape(n, 3)).cuda().float()
+                        
+                        if get_foot_contact:
+                            max_length = 128
+                            s, r = n//max_length, n%max_length
+                            #print(n, s, r)
+                            all_tensor = []
+                            for i in range(s):
+                                with torch.no_grad():
+                                    joints = self.smplx(
+                                        betas=betas[i*max_length:(i+1)*max_length], 
+                                        transl=trans[i*max_length:(i+1)*max_length], 
+                                        expression=exps[i*max_length:(i+1)*max_length], 
+                                        jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], 
+                                        global_orient=poses[i*max_length:(i+1)*max_length,:3], 
+                                        body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], 
+                                        left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], 
+                                        right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], 
+                                        return_verts=True,
+                                        return_joints=True,
+                                        leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], 
+                                        reye_pose=poses[i*max_length:(i+1)*max_length, 72:75],
+                                    )['joints'][:, (7,8,10,11), :].reshape(max_length, 4, 3).cpu()
+                                all_tensor.append(joints)
+                            if r != 0:
+                                with torch.no_grad():
+                                    joints = self.smplx(
+                                        betas=betas[s*max_length:s*max_length+r], 
+                                        transl=trans[s*max_length:s*max_length+r], 
+                                        expression=exps[s*max_length:s*max_length+r], 
+                                        jaw_pose=poses[s*max_length:s*max_length+r, 66:69], 
+                                        global_orient=poses[s*max_length:s*max_length+r,:3], 
+                                        body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], 
+                                        left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], 
+                                        right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], 
+                                        return_verts=True,
+                                        return_joints=True,
+                                        leye_pose=poses[s*max_length:s*max_length+r, 69:72], 
+                                        reye_pose=poses[s*max_length:s*max_length+r, 72:75],
+                                    )['joints'][:, (7,8,10,11), :].reshape(r, 4, 3).cpu()
+                                all_tensor.append(joints)
+                            joints = torch.cat(all_tensor, axis=0) # all, 4, 3
+                            # print(joints.shape)
+                            feetv = torch.zeros(joints.shape[1], joints.shape[0])
+                            joints = joints.permute(1, 0, 2)
+                            #print(joints.shape, feetv.shape)
+                            feetv[:, :-1] = (joints[:, 1:] - joints[:, :-1]).norm(dim=-1)
+                            #print(feetv.shape)
+                            contacts = (feetv < 0.01).numpy().astype(float)
+                            # print(contacts.shape, contacts)
+                            contacts = contacts.transpose(1, 0)[::stride]
+                            pose_each_file = pose_each_file * self.joint_mask
+                            pose_each_file = pose_each_file[:, self.joint_mask.astype(bool)]
+                            pose_each_file = np.concatenate([pose_each_file, contacts], axis=1)
+                            # print(pose_each_file.shape)
+                        else:
+                            pose_each_file = pose_each_file * self.joint_mask
+                            pose_each_file = pose_each_file[:, self.joint_mask.astype(bool)]
+                
+                        # print(pose_each_file.shape)
+
+                                
+                    if self.args.id_rep is not None:
+                        vid_each_file = np.repeat(np.array(int(100)-1).reshape(1, 1), pose_each_file.shape[0], axis=0)
+            
+                    filtered_result = self._sample_from_clip(
+                        dst_lmdb_env,
+                        audio_each_file, pose_each_file, trans_each_file, trans_v_each_file,shape_each_file, facial_each_file, word_each_file,
+                        vid_each_file, emo_each_file, sem_each_file,
+                        disable_filtering, clean_first_seconds, clean_final_seconds, is_test,
+                        ) 
+                    for type in filtered_result.keys():
+                        n_filtered_out[type] += filtered_result[type]       
+
+
+                         
+        with dst_lmdb_env.begin() as txn:
+            logger.info(colored(f"no. of samples: {txn.stat()['entries']}", "cyan"))
+            n_total_filtered = 0
+            for type, n_filtered in n_filtered_out.items():
+                logger.info("{}: {}".format(type, n_filtered))
+                n_total_filtered += n_filtered
+            logger.info(colored("no. of excluded samples: {} ({:.1f}%)".format(
+                n_total_filtered, 100 * n_total_filtered / (txn.stat()["entries"] + n_total_filtered)), "cyan"))
+        dst_lmdb_env.sync()
+        dst_lmdb_env.close()
+    
+    def _sample_from_clip(
+        self, dst_lmdb_env, audio_each_file, pose_each_file, trans_each_file, trans_v_each_file,shape_each_file, facial_each_file, word_each_file,
+        vid_each_file, emo_each_file, sem_each_file,
+        disable_filtering, clean_first_seconds, clean_final_seconds, is_test,
+        ):
+        """
+        for data cleaning, we ignore the data for first and final n s
+        for test, we return all data 
+        """
+        # audio_start = int(self.alignment[0] * self.args.audio_fps)
+        # pose_start = int(self.alignment[1] * self.args.pose_fps)
+        #logger.info(f"before: {audio_each_file.shape} {pose_each_file.shape}")
+        # audio_each_file = audio_each_file[audio_start:]
+        # pose_each_file = pose_each_file[pose_start:]
+        # trans_each_file = 
+        #logger.info(f"after alignment: {audio_each_file.shape} {pose_each_file.shape}")
+        #print(pose_each_file.shape)
+        round_seconds_skeleton = pose_each_file.shape[0] // self.args.pose_fps  # assume 1500 frames / 15 fps = 100 s
+        #print(round_seconds_skeleton)
+        if audio_each_file != []:
+            if self.args.audio_rep != "wave16k":
+                round_seconds_audio = len(audio_each_file) // self.args.audio_fps # assume 16,000,00 / 16,000 = 100 s
+            elif self.args.audio_rep == "mfcc":
+                round_seconds_audio = audio_each_file.shape[0] // self.args.audio_fps
+            else:
+                round_seconds_audio = audio_each_file.shape[0] // self.args.audio_sr
+            if facial_each_file != []:
+                round_seconds_facial = facial_each_file.shape[0] // self.args.pose_fps
+                logger.info(f"audio: {round_seconds_audio}s, pose: {round_seconds_skeleton}s, facial: {round_seconds_facial}s")
+                round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton, round_seconds_facial)
+                max_round = max(round_seconds_audio, round_seconds_skeleton, round_seconds_facial)
+                if round_seconds_skeleton != max_round: 
+                    logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s")  
+            else:
+                logger.info(f"pose: {round_seconds_skeleton}s, audio: {round_seconds_audio}s")
+                round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton)
+                max_round = max(round_seconds_audio, round_seconds_skeleton)
+                if round_seconds_skeleton != max_round: 
+                    logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s")
+        
+        clip_s_t, clip_e_t = clean_first_seconds, round_seconds_skeleton - clean_final_seconds # assume [10, 90]s
+        clip_s_f_audio, clip_e_f_audio = self.args.audio_fps * clip_s_t, clip_e_t * self.args.audio_fps # [160,000,90*160,000]
+        clip_s_f_pose, clip_e_f_pose = clip_s_t * self.args.pose_fps, clip_e_t * self.args.pose_fps # [150,90*15]
+        
+        
+        for ratio in self.args.multi_length_training:
+            if is_test:# stride = length for test
+                cut_length = clip_e_f_pose - clip_s_f_pose
+                self.args.stride = cut_length
+                self.max_length = cut_length
+            else:
+                self.args.stride = int(ratio*self.ori_stride)
+                cut_length = int(self.ori_length*ratio)
+                
+            num_subdivision = math.floor((clip_e_f_pose - clip_s_f_pose - cut_length) / self.args.stride) + 1
+            logger.info(f"pose from frame {clip_s_f_pose} to {clip_e_f_pose}, length {cut_length}")
+            logger.info(f"{num_subdivision} clips is expected with stride {self.args.stride}")
+            
+            if audio_each_file != []:
+                audio_short_length = math.floor(cut_length / self.args.pose_fps * self.args.audio_fps)
+                """
+                for audio sr = 16000, fps = 15, pose_length = 34, 
+                audio short length = 36266.7 -> 36266
+                this error is fine.
+                """
+                logger.info(f"audio from frame {clip_s_f_audio} to {clip_e_f_audio}, length {audio_short_length}")
+             
+            n_filtered_out = defaultdict(int)
+            sample_pose_list = []
+            sample_audio_list = []
+            sample_facial_list = []
+            sample_shape_list = []
+            sample_word_list = []
+            sample_emo_list = []
+            sample_sem_list = []
+            sample_vid_list = []
+            sample_trans_list = []
+            sample_trans_v_list = []
+           
+            for i in range(num_subdivision): # cut into around 2s chip, (self npose)
+                start_idx = clip_s_f_pose + i * self.args.stride
+                fin_idx = start_idx + cut_length 
+                sample_pose = pose_each_file[start_idx:fin_idx]
+
+                sample_trans = trans_each_file[start_idx:fin_idx]
+                sample_trans_v = trans_v_each_file[start_idx:fin_idx]
+                sample_shape = shape_each_file[start_idx:fin_idx]
+                # print(sample_pose.shape)
+                if self.args.audio_rep is not None and audio_each_file != []:
+                    audio_start = clip_s_f_audio + math.floor(i * self.args.stride * self.args.audio_fps / self.args.pose_fps)
+                    audio_end = audio_start + audio_short_length
+                    sample_audio = audio_each_file[audio_start:audio_end]
+                else:
+                    sample_audio = np.array([-1])
+                sample_facial = facial_each_file[start_idx:fin_idx] if self.args.facial_rep is not None else np.array([-1])
+                sample_word = word_each_file[start_idx:fin_idx] if self.args.word_rep is not None else np.array([-1])
+                sample_emo = emo_each_file[start_idx:fin_idx] if self.args.emo_rep is not None else np.array([-1])
+                sample_sem = sem_each_file[start_idx:fin_idx] if self.args.sem_rep is not None else np.array([-1])
+                sample_vid = vid_each_file[start_idx:fin_idx] if self.args.id_rep is not None else np.array([-1])
+                
+                if sample_pose.any() != None:
+                    # filtering motion skeleton data
+                    sample_pose, filtering_message = MotionPreprocessor(sample_pose).get()
+                    is_correct_motion = (sample_pose != [])
+                    if is_correct_motion or disable_filtering:
+                        sample_pose_list.append(sample_pose)
+                        sample_audio_list.append(sample_audio)
+                        sample_facial_list.append(sample_facial)
+                        sample_shape_list.append(sample_shape)
+                        sample_word_list.append(sample_word)
+                        sample_vid_list.append(sample_vid)
+                        sample_emo_list.append(sample_emo)
+                        sample_sem_list.append(sample_sem)
+                        sample_trans_list.append(sample_trans)
+                        sample_trans_v_list.append(sample_trans_v)
+                    else:
+                        n_filtered_out[filtering_message] += 1
+
+            if len(sample_pose_list) > 0:
+                with dst_lmdb_env.begin(write=True) as txn:
+                    for pose, audio, facial, shape, word, vid, emo, sem, trans,trans_v in zip(
+                        sample_pose_list,
+                        sample_audio_list,
+                        sample_facial_list,
+                        sample_shape_list,
+                        sample_word_list,
+                        sample_vid_list,
+                        sample_emo_list,
+                        sample_sem_list,
+                        sample_trans_list,
+                        sample_trans_v_list,):
+                        k = "{:005}".format(self.n_out_samples).encode("ascii")
+                        v = [pose, audio, facial, shape, word, emo, sem, vid, trans,trans_v]
+                        v = pickle.dumps(v,5)
+                        txn.put(k, v)
+                        self.n_out_samples += 1
+        return n_filtered_out
+
+    def __getitem__(self, idx):
+        with self.lmdb_env.begin(write=False) as txn:
+            key = "{:005}".format(idx).encode("ascii")
+            sample = txn.get(key)
+            sample = pickle.loads(sample)
+            tar_pose, in_audio, in_facial, in_shape, in_word, emo, sem, vid, trans,trans_v = sample
+            #print(in_shape)
+            #vid = torch.from_numpy(vid).int()
+            emo = torch.from_numpy(emo).int()
+            sem = torch.from_numpy(sem).float() 
+            in_audio = np.zeros([68266,2])
+            in_audio = torch.from_numpy(in_audio).float()
+            in_word = np.zeros([128])
+            in_facial = np.zeros([128,100])
+            in_word = torch.from_numpy(in_word).float() if self.args.word_cache else torch.from_numpy(in_word).int() 
+            if self.loader_type == "test":
+                tar_pose = torch.from_numpy(tar_pose).float()
+                trans = torch.from_numpy(trans).float()
+                trans_v = torch.from_numpy(trans_v).float()
+                in_facial = torch.from_numpy(in_facial).float()
+                vid = torch.from_numpy(vid).float()
+                in_shape = torch.from_numpy(in_shape).float()
+            else:
+                in_shape = torch.from_numpy(in_shape).reshape((in_shape.shape[0], -1)).float()
+                trans = torch.from_numpy(trans).reshape((trans.shape[0], -1)).float()
+                trans_v = torch.from_numpy(trans_v).reshape((trans_v.shape[0], -1)).float()
+                vid = torch.from_numpy(vid).reshape((vid.shape[0], -1)).float()
+                tar_pose = torch.from_numpy(tar_pose).reshape((tar_pose.shape[0], -1)).float()
+                in_facial = torch.from_numpy(in_facial).reshape((in_facial.shape[0], -1)).float()
+            return {"pose":tar_pose, "audio":in_audio, "facial":in_facial, "beta": in_shape, "word":in_word, "id":vid, "emo":emo, "sem":sem, "trans":trans,"trans_v":trans_v}
+
+         
+class MotionPreprocessor:
+    def __init__(self, skeletons):
+        self.skeletons = skeletons
+        #self.mean_pose = mean_pose
+        self.filtering_message = "PASS"
+
+    def get(self):
+        assert (self.skeletons is not None)
+
+        # filtering
+        if self.skeletons != []:
+            if self.check_pose_diff():
+                self.skeletons = []
+                self.filtering_message = "pose"
+            # elif self.check_spine_angle():
+            #     self.skeletons = []
+            #     self.filtering_message = "spine angle"
+            # elif self.check_static_motion():
+            #     self.skeletons = []
+            #     self.filtering_message = "motion"
+
+        # if self.skeletons != []:
+        #     self.skeletons = self.skeletons.tolist()
+        #     for i, frame in enumerate(self.skeletons):
+        #         assert not np.isnan(self.skeletons[i]).any()  # missing joints
+
+        return self.skeletons, self.filtering_message
+
+    def check_static_motion(self, verbose=True):
+        def get_variance(skeleton, joint_idx):
+            wrist_pos = skeleton[:, joint_idx]
+            variance = np.sum(np.var(wrist_pos, axis=0))
+            return variance
+
+        left_arm_var = get_variance(self.skeletons, 6)
+        right_arm_var = get_variance(self.skeletons, 9)
+
+        th = 0.0014  # exclude 13110
+        # th = 0.002  # exclude 16905
+        if left_arm_var < th and right_arm_var < th:
+            if verbose:
+                print("skip - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var))
+            return True
+        else:
+            if verbose:
+                print("pass - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var))
+            return False
+
+
+    def check_pose_diff(self, verbose=False):
+#         diff = np.abs(self.skeletons - self.mean_pose) # 186*1
+#         diff = np.mean(diff)
+
+#         # th = 0.017
+#         th = 0.02 #0.02  # exclude 3594
+#         if diff < th:
+#             if verbose:
+#                 print("skip - check_pose_diff {:.5f}".format(diff))
+#             return True
+# #         th = 3.5 #0.02  # exclude 3594
+# #         if 3.5 < diff < 5:
+# #             if verbose:
+# #                 print("skip - check_pose_diff {:.5f}".format(diff))
+# #             return True
+#         else:
+#             if verbose:
+#                 print("pass - check_pose_diff {:.5f}".format(diff))
+        return False
+
+
+    def check_spine_angle(self, verbose=True):
+        def angle_between(v1, v2):
+            v1_u = v1 / np.linalg.norm(v1)
+            v2_u = v2 / np.linalg.norm(v2)
+            return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0))
+
+        angles = []
+        for i in range(self.skeletons.shape[0]):
+            spine_vec = self.skeletons[i, 1] - self.skeletons[i, 0]
+            angle = angle_between(spine_vec, [0, -1, 0])
+            angles.append(angle)
+
+        if np.rad2deg(max(angles)) > 30 or np.rad2deg(np.mean(angles)) > 20:  # exclude 4495
+        # if np.rad2deg(max(angles)) > 20:  # exclude 8270
+            if verbose:
+                print("skip - check_spine_angle {:.5f}, {:.5f}".format(max(angles), np.mean(angles)))
+            return True
+        else:
+            if verbose:
+                print("pass - check_spine_angle {:.5f}".format(max(angles)))
+            return False

dataloaders/beat_sep.py

@@ -0,0 +1,772 @@
+import os
+import pickle
+import math
+import shutil
+import numpy as np
+import lmdb as lmdb
+import textgrid as tg
+import pandas as pd
+import torch
+import glob
+import json
+from termcolor import colored
+from loguru import logger
+from collections import defaultdict
+from torch.utils.data import Dataset
+import torch.distributed as dist
+#import pyarrow
+import pickle
+import librosa
+import smplx
+
+from .build_vocab import Vocab
+from .utils.audio_features import Wav2Vec2Model
+from .data_tools import joints_list
+from .utils import rotation_conversions as rc
+from .utils import other_tools
+
+class CustomDataset(Dataset):
+    def __init__(self, args, loader_type, augmentation=None, kwargs=None, build_cache=True):
+        self.args = args
+        self.loader_type = loader_type
+
+        self.rank = dist.get_rank()
+        self.ori_stride = self.args.stride
+        self.ori_length = self.args.pose_length
+        self.alignment = [0,0] # for trinity
+        
+        self.ori_joint_list = joints_list[self.args.ori_joints]
+        self.tar_joint_list = joints_list[self.args.tar_joints]
+        if 'smplx' in self.args.pose_rep:
+            self.joint_mask = np.zeros(len(list(self.ori_joint_list.keys()))*3)
+            self.joints = len(list(self.tar_joint_list.keys()))  
+            for joint_name in self.tar_joint_list:
+                self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        else:
+            self.joints = len(list(self.ori_joint_list.keys()))+1
+            self.joint_mask = np.zeros(self.joints*3)
+            for joint_name in self.tar_joint_list:
+                if joint_name == "Hips":
+                    self.joint_mask[3:6] = 1
+                else:
+                    self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        # select trainable joints
+        
+        split_rule = pd.read_csv(args.data_path+"train_test_split.csv")
+        self.selected_file = split_rule.loc[(split_rule['type'] == loader_type) & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+        if args.additional_data and loader_type == 'train':
+            split_b = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            #self.selected_file = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            self.selected_file = pd.concat([self.selected_file, split_b])
+        if self.selected_file.empty:
+            logger.warning(f"{loader_type} is empty for speaker {self.args.training_speakers}, use train set 0-8 instead")
+            self.selected_file = split_rule.loc[(split_rule['type'] == 'train') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            self.selected_file = self.selected_file.iloc[0:8]
+        self.data_dir = args.data_path 
+        
+        if loader_type == "test": 
+            self.args.multi_length_training = [1.0]
+        self.max_length = int(args.pose_length * self.args.multi_length_training[-1])
+        self.max_audio_pre_len = math.floor(args.pose_length / args.pose_fps * self.args.audio_sr)
+        if self.max_audio_pre_len > self.args.test_length*self.args.audio_sr: 
+            self.max_audio_pre_len = self.args.test_length*self.args.audio_sr
+        
+        if args.word_rep is not None:
+            with open(f"{args.data_path}weights/vocab.pkl", 'rb') as f:
+                self.lang_model = pickle.load(f)
+                
+        preloaded_dir = self.args.root_path + self.args.cache_path + loader_type + f"/{args.pose_rep}_cache"      
+        # if args.pose_norm:
+        #     # careful for rotation vectors
+        #     if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy"):
+        #         self.calculate_mean_pose()
+        #     self.mean_pose = np.load(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy")
+        #     self.std_pose = np.load(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_std.npy")
+        # if args.audio_norm:
+        #     if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/bvh_mean.npy"):
+        #         self.calculate_mean_audio()
+        #     self.mean_audio = np.load(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/npy_mean.npy")
+        #     self.std_audio = np.load(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/npy_std.npy")
+        # if args.facial_norm:
+        #     if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy"):
+        #         self.calculate_mean_face()
+        #     self.mean_facial = np.load(args.data_path+args.mean_pose_path+f"{args.facial_rep}/json_mean.npy")
+        #     self.std_facial = np.load(args.data_path+args.mean_pose_path+f"{args.facial_rep}/json_std.npy")
+        if self.args.beat_align:
+            if not os.path.exists(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy"):
+                self.calculate_mean_velocity(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy")
+            self.avg_vel = np.load(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy")
+            
+        if build_cache and self.rank == 0:
+            self.build_cache(preloaded_dir)
+        self.lmdb_env = lmdb.open(preloaded_dir, readonly=True, lock=False)
+        with self.lmdb_env.begin() as txn:
+            self.n_samples = txn.stat()["entries"] 
+
+    
+    def calculate_mean_velocity(self, save_path):
+        self.smplx = smplx.create(
+            self.args.data_path_1+"smplx_models/", 
+            model_type='smplx',
+            gender='NEUTRAL_2020', 
+            use_face_contour=False,
+            num_betas=300,
+            num_expression_coeffs=100, 
+            ext='npz',
+            use_pca=False,
+        ).cuda().eval()
+        dir_p = self.data_dir + self.args.pose_rep + "/"
+        all_list = []
+        from tqdm import tqdm
+        for tar in tqdm(os.listdir(dir_p)):
+            if tar.endswith(".npz"):
+                m_data = np.load(dir_p+tar, allow_pickle=True)
+                betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"]
+                n, c = poses.shape[0], poses.shape[1]
+                betas = betas.reshape(1, 300)
+                betas = np.tile(betas, (n, 1))
+                betas = torch.from_numpy(betas).cuda().float()
+                poses = torch.from_numpy(poses.reshape(n, c)).cuda().float()
+                exps = torch.from_numpy(exps.reshape(n, 100)).cuda().float()
+                trans = torch.from_numpy(trans.reshape(n, 3)).cuda().float()
+                max_length = 128
+                s, r = n//max_length, n%max_length
+                #print(n, s, r)
+                all_tensor = []
+                for i in range(s):
+                    with torch.no_grad():
+                        joints = self.smplx(
+                            betas=betas[i*max_length:(i+1)*max_length], 
+                            transl=trans[i*max_length:(i+1)*max_length], 
+                            expression=exps[i*max_length:(i+1)*max_length], 
+                            jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], 
+                            global_orient=poses[i*max_length:(i+1)*max_length,:3], 
+                            body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], 
+                            left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], 
+                            right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], 
+                            return_verts=True,
+                            return_joints=True,
+                            leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], 
+                            reye_pose=poses[i*max_length:(i+1)*max_length, 72:75],
+                        )['joints'][:, :55, :].reshape(max_length, 55*3)
+                    all_tensor.append(joints)
+                if r != 0:
+                    with torch.no_grad():
+                        joints = self.smplx(
+                            betas=betas[s*max_length:s*max_length+r], 
+                            transl=trans[s*max_length:s*max_length+r], 
+                            expression=exps[s*max_length:s*max_length+r], 
+                            jaw_pose=poses[s*max_length:s*max_length+r, 66:69], 
+                            global_orient=poses[s*max_length:s*max_length+r,:3], 
+                            body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], 
+                            left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], 
+                            right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], 
+                            return_verts=True,
+                            return_joints=True,
+                            leye_pose=poses[s*max_length:s*max_length+r, 69:72], 
+                            reye_pose=poses[s*max_length:s*max_length+r, 72:75],
+                        )['joints'][:, :55, :].reshape(r, 55*3)
+                    all_tensor.append(joints)
+                joints = torch.cat(all_tensor, axis=0)
+                joints = joints.permute(1, 0)
+                dt = 1/30
+            # first steps is forward diff (t+1 - t) / dt
+                init_vel = (joints[:, 1:2] - joints[:, :1]) / dt
+                # middle steps are second order (t+1 - t-1) / 2dt
+                middle_vel = (joints[:, 2:] - joints[:, 0:-2]) / (2 * dt)
+                # last step is backward diff (t - t-1) / dt
+                final_vel = (joints[:, -1:] - joints[:, -2:-1]) / dt
+                #print(joints.shape, init_vel.shape, middle_vel.shape, final_vel.shape)
+                vel_seq = torch.cat([init_vel, middle_vel, final_vel], dim=1).permute(1, 0).reshape(n, 55, 3)
+                #print(vel_seq.shape)
+                #.permute(1, 0).reshape(n, 55, 3)
+                vel_seq_np = vel_seq.cpu().numpy()
+                vel_joints_np = np.linalg.norm(vel_seq_np, axis=2) # n * 55
+                all_list.append(vel_joints_np)
+        avg_vel = np.mean(np.concatenate(all_list, axis=0),axis=0) # 55
+        np.save(save_path, avg_vel)
+        
+    
+    def build_cache(self, preloaded_dir):
+        logger.info(f"Audio bit rate: {self.args.audio_fps}")
+        logger.info("Reading data '{}'...".format(self.data_dir))
+        logger.info("Creating the dataset cache...")
+        if self.args.new_cache:
+            if os.path.exists(preloaded_dir):
+                shutil.rmtree(preloaded_dir)
+        if os.path.exists(preloaded_dir):
+            logger.info("Found the cache {}".format(preloaded_dir))
+        elif self.loader_type == "test":
+            self.cache_generation(
+                preloaded_dir, True, 
+                0, 0,
+                is_test=True)
+        else:
+            self.cache_generation(
+                preloaded_dir, self.args.disable_filtering, 
+                self.args.clean_first_seconds, self.args.clean_final_seconds,
+                is_test=False)
+        
+    def __len__(self):
+        return self.n_samples
+    
+
+    def cache_generation(self, out_lmdb_dir, disable_filtering, clean_first_seconds,  clean_final_seconds, is_test=False):
+        # if "wav2vec2" in self.args.audio_rep:
+        #     self.wav2vec_model = Wav2Vec2Model.from_pretrained(f"{self.args.data_path_1}/hub/transformer/wav2vec2-base-960h")
+        #     self.wav2vec_model.feature_extractor._freeze_parameters()
+        #     self.wav2vec_model = self.wav2vec_model.cuda()
+        #     self.wav2vec_model.eval()
+        
+        self.n_out_samples = 0
+        # create db for samples
+        if not os.path.exists(out_lmdb_dir): os.makedirs(out_lmdb_dir)
+        dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 50))# 50G
+        n_filtered_out = defaultdict(int)
+    
+        for index, file_name in self.selected_file.iterrows():
+            f_name = file_name["id"]
+            ext = ".npz" if "smplx" in self.args.pose_rep else ".bvh"
+            pose_file = self.data_dir + self.args.pose_rep + "/" + f_name + ext
+            pose_each_file = []
+            trans_each_file = []
+            shape_each_file = []
+            audio_each_file = []
+            facial_each_file = []
+            word_each_file = []
+            emo_each_file = []
+            sem_each_file = []
+            vid_each_file = []
+            id_pose = f_name #1_wayne_0_1_1
+            
+            logger.info(colored(f"# ---- Building cache for Pose   {id_pose} ---- #", "blue"))
+            if "smplx" in self.args.pose_rep:
+                pose_data = np.load(pose_file, allow_pickle=True)
+                assert 30%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 30'
+                stride = int(30/self.args.pose_fps)
+                pose_each_file = pose_data["poses"][::stride] * self.joint_mask
+                pose_each_file = pose_each_file[:, self.joint_mask.astype(bool)]
+                # print(pose_each_file.shape)
+                trans_each_file = pose_data["trans"][::stride]
+                shape_each_file = np.repeat(pose_data["betas"].reshape(1, 300), pose_each_file.shape[0], axis=0)
+                if self.args.facial_rep is not None:
+                    logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #")
+                    facial_each_file = pose_data["expressions"][::stride]
+                    if self.args.facial_norm: 
+                        facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial
+                    
+            else:
+                assert 120%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120'
+                stride = int(120/self.args.pose_fps)
+                with open(pose_file, "r") as pose_data:
+                    for j, line in enumerate(pose_data.readlines()):
+                        if j < 431: continue     
+                        if j%stride != 0:continue
+                        data = np.fromstring(line, dtype=float, sep=" ")
+                        rot_data = rc.euler_angles_to_matrix(torch.from_numpy(np.deg2rad(data)).reshape(-1, self.joints,3), "XYZ")
+                        rot_data = rc.matrix_to_axis_angle(rot_data).reshape(-1, self.joints*3) 
+                        rot_data = rot_data.numpy() * self.joint_mask
+                        
+                        pose_each_file.append(rot_data)
+                        trans_each_file.append(data[:3])
+                        
+                pose_each_file = np.array(pose_each_file)
+                # print(pose_each_file.shape)
+                trans_each_file = np.array(trans_each_file)
+                shape_each_file = np.repeat(np.array(-1).reshape(1, 1), pose_each_file.shape[0], axis=0)
+                if self.args.facial_rep is not None:
+                    logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #")
+                    facial_file = pose_file.replace(self.args.pose_rep, self.args.facial_rep).replace("bvh", "json")
+                    assert 60%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120'
+                    stride = int(60/self.args.pose_fps)
+                    if not os.path.exists(facial_file):
+                        logger.warning(f"# ---- file not found for Facial {id_pose}, skip all files with the same id ---- #")
+                        self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index)
+                        continue
+                    with open(facial_file, 'r') as facial_data_file:
+                        facial_data = json.load(facial_data_file)
+                        for j, frame_data in enumerate(facial_data['frames']):
+                            if j%stride != 0:continue
+                            facial_each_file.append(frame_data['weights'])
+                    facial_each_file = np.array(facial_each_file)
+                    if self.args.facial_norm: 
+                        facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial
+                        
+            if self.args.id_rep is not None:
+                vid_each_file = np.repeat(np.array(int(f_name.split("_")[0])-1).reshape(1, 1), pose_each_file.shape[0], axis=0)
+      
+            if self.args.audio_rep is not None:
+                logger.info(f"# ---- Building cache for Audio  {id_pose} and Pose {id_pose} ---- #")
+                audio_file = pose_file.replace(self.args.pose_rep, 'wave16k').replace(ext, ".wav")
+                if not os.path.exists(audio_file):
+                    logger.warning(f"# ---- file not found for Audio  {id_pose}, skip all files with the same id ---- #")
+                    self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index)
+                    continue
+                audio_each_file, sr = librosa.load(audio_file)
+                audio_each_file = librosa.resample(audio_each_file, orig_sr=sr, target_sr=self.args.audio_sr)
+                if self.args.audio_rep == "onset+amplitude":
+                    from numpy.lib import stride_tricks
+                    frame_length = 1024
+                    # hop_length = 512
+                    shape = (audio_each_file.shape[-1] - frame_length + 1, frame_length)
+                    strides = (audio_each_file.strides[-1], audio_each_file.strides[-1])
+                    rolling_view = stride_tricks.as_strided(audio_each_file, shape=shape, strides=strides)
+                    amplitude_envelope = np.max(np.abs(rolling_view), axis=1)
+                    # pad the last frame_length-1 samples
+                    amplitude_envelope = np.pad(amplitude_envelope, (0, frame_length-1), mode='constant', constant_values=amplitude_envelope[-1])
+                    audio_onset_f = librosa.onset.onset_detect(y=audio_each_file, sr=self.args.audio_sr, units='frames')
+                    onset_array = np.zeros(len(audio_each_file), dtype=float)
+                    onset_array[audio_onset_f] = 1.0
+                    # print(amplitude_envelope.shape, audio_each_file.shape, onset_array.shape)
+                    audio_each_file = np.concatenate([amplitude_envelope.reshape(-1, 1), onset_array.reshape(-1, 1)], axis=1)
+                elif self.args.audio_rep == "mfcc":
+                    audio_each_file = librosa.feature.melspectrogram(y=audio_each_file, sr=self.args.audio_sr, n_mels=128, hop_length=int(self.args.audio_sr/self.args.audio_fps))
+                    audio_each_file = audio_each_file.transpose(1, 0)
+                    # print(audio_each_file.shape, pose_each_file.shape)
+                if self.args.audio_norm and self.args.audio_rep == "wave16k": 
+                    audio_each_file = (audio_each_file - self.mean_audio) / self.std_audio
+                # print(audio_each_file.shape)
+            time_offset = 0
+            if self.args.word_rep is not None:
+                logger.info(f"# ---- Building cache for Word   {id_pose} and Pose {id_pose} ---- #")
+                word_file = f"{self.data_dir}{self.args.word_rep}/{id_pose}.TextGrid"
+                if not os.path.exists(word_file):
+                    logger.warning(f"# ---- file not found for Word   {id_pose}, skip all files with the same id ---- #")
+                    self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index)
+                    continue
+                tgrid = tg.TextGrid.fromFile(word_file)
+                if self.args.t_pre_encoder == "bert":
+                    from transformers import AutoTokenizer, BertModel
+                    tokenizer = AutoTokenizer.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True)
+                    model = BertModel.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True).eval()
+                    list_word = []
+                    all_hidden = []
+                    max_len = 400
+                    last = 0
+                    word_token_mapping = []
+                    first = True
+                    for i, word in enumerate(tgrid[0]):
+                        last = i
+                        if (i%max_len != 0) or (i==0):
+                            if word.mark == "":
+                                list_word.append(".")
+                            else:
+                                list_word.append(word.mark)
+                        else:
+                            max_counter = max_len
+                            str_word = ' '.join(map(str, list_word))
+                            if first:
+                                global_len = 0
+                            end = -1
+                            offset_word = []
+                            for k, wordvalue in enumerate(list_word):
+                                start = end+1 
+                                end = start+len(wordvalue)
+                                offset_word.append((start, end))
+                            #print(offset_word)
+                            token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping']
+                            #print(token_scan)
+                            for start, end in offset_word:
+                                sub_mapping = []
+                                for i, (start_t, end_t) in enumerate(token_scan[1:-1]):
+                                    if int(start) <= int(start_t) and int(end_t) <= int(end):
+                                        #print(i+global_len)
+                                        sub_mapping.append(i+global_len)
+                                word_token_mapping.append(sub_mapping)
+                            #print(len(word_token_mapping))
+                            global_len = word_token_mapping[-1][-1] + 1    
+                            list_word = []
+                            if word.mark == "":
+                                list_word.append(".")
+                            else:
+                                list_word.append(word.mark)
+                            
+                            with torch.no_grad():
+                                inputs = tokenizer(str_word, return_tensors="pt")
+                                outputs = model(**inputs)
+                                last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :]
+                            all_hidden.append(last_hidden_states)
+                     
+                    #list_word = list_word[:10]
+                    if list_word == []:
+                        pass
+                    else:
+                        if first: 
+                            global_len = 0
+                        str_word = ' '.join(map(str, list_word))
+                        end = -1
+                        offset_word = []
+                        for k, wordvalue in enumerate(list_word):
+                            start = end+1 
+                            end = start+len(wordvalue)
+                            offset_word.append((start, end))
+                        #print(offset_word)
+                        token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping']
+                        #print(token_scan)
+                        for start, end in offset_word:
+                            sub_mapping = []
+                            for i, (start_t, end_t) in enumerate(token_scan[1:-1]):
+                                if int(start) <= int(start_t) and int(end_t) <= int(end):
+                                    sub_mapping.append(i+global_len)
+                                    #print(sub_mapping)
+                            word_token_mapping.append(sub_mapping)
+                        #print(len(word_token_mapping))
+                        with torch.no_grad():
+                            inputs = tokenizer(str_word, return_tensors="pt")
+                            outputs = model(**inputs)
+                            last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :]
+                        all_hidden.append(last_hidden_states)
+                    last_hidden_states = np.concatenate(all_hidden, axis=0)
+            
+                for i in range(pose_each_file.shape[0]):
+                    found_flag = False
+                    current_time = i/self.args.pose_fps + time_offset
+                    j_last = 0
+                    for j, word in enumerate(tgrid[0]): 
+                        word_n, word_s, word_e = word.mark, word.minTime, word.maxTime
+                        if word_s<=current_time and current_time<=word_e:
+                            if self.args.word_cache and self.args.t_pre_encoder == 'bert':
+                                mapping_index = word_token_mapping[j]
+                                #print(mapping_index, word_s, word_e)
+                                s_t = np.linspace(word_s, word_e, len(mapping_index)+1)
+                                #print(s_t)
+                                for tt, t_sep in enumerate(s_t[1:]):
+                                    if current_time <= t_sep:
+                                        #if len(mapping_index) > 1: print(mapping_index[tt])
+                                        word_each_file.append(last_hidden_states[mapping_index[tt]])
+                                        break
+                            else:
+                                if word_n == " ":
+                                    word_each_file.append(self.lang_model.PAD_token)
+                                else:
+                                    word_each_file.append(self.lang_model.get_word_index(word_n))
+                            found_flag = True
+                            j_last = j
+                            break
+                        else: continue   
+                    if not found_flag: 
+                        if self.args.word_cache and self.args.t_pre_encoder == 'bert':
+                            word_each_file.append(last_hidden_states[j_last])
+                        else:
+                            word_each_file.append(self.lang_model.UNK_token)
+                word_each_file = np.array(word_each_file)
+                #print(word_each_file.shape)
+                
+            if self.args.emo_rep is not None:
+                logger.info(f"# ---- Building cache for Emo    {id_pose} and Pose {id_pose} ---- #")
+                rtype, start = int(id_pose.split('_')[3]), int(id_pose.split('_')[3])
+                if rtype == 0 or rtype == 2 or rtype == 4 or rtype == 6:
+                    if start >= 1 and start <= 64:
+                        score = 0
+                    elif start >= 65 and start <= 72:
+                        score = 1
+                    elif start >= 73 and start <= 80:
+                        score = 2
+                    elif start >= 81 and start <= 86:
+                        score = 3
+                    elif start >= 87 and start <= 94:
+                        score = 4
+                    elif start >= 95 and start <= 102:
+                        score = 5
+                    elif start >= 103 and start <= 110:
+                        score = 6
+                    elif start >= 111 and start <= 118:
+                        score = 7
+                    else: pass
+                else:
+                    # you may denote as unknown in the future
+                    score = 0
+                emo_each_file = np.repeat(np.array(score).reshape(1, 1), pose_each_file.shape[0], axis=0)    
+                #print(emo_each_file)
+                
+            if self.args.sem_rep is not None:
+                logger.info(f"# ---- Building cache for Sem    {id_pose} and Pose {id_pose} ---- #")
+                sem_file = f"{self.data_dir}{self.args.sem_rep}/{id_pose}.txt" 
+                sem_all = pd.read_csv(sem_file, 
+                    sep='\t', 
+                    names=["name", "start_time", "end_time", "duration", "score", "keywords"])
+                # we adopt motion-level semantic score here. 
+                for i in range(pose_each_file.shape[0]):
+                    found_flag = False
+                    for j, (start, end, score) in enumerate(zip(sem_all['start_time'],sem_all['end_time'], sem_all['score'])):
+                        current_time = i/self.args.pose_fps + time_offset
+                        if start<=current_time and current_time<=end: 
+                            sem_each_file.append(score)
+                            found_flag=True
+                            break
+                        else: continue 
+                    if not found_flag: sem_each_file.append(0.)
+                sem_each_file = np.array(sem_each_file)
+                #print(sem_each_file)
+            
+            filtered_result = self._sample_from_clip(
+                dst_lmdb_env,
+                audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file,
+                vid_each_file, emo_each_file, sem_each_file,
+                disable_filtering, clean_first_seconds, clean_final_seconds, is_test,
+                ) 
+            for type in filtered_result.keys():
+                n_filtered_out[type] += filtered_result[type]
+                                
+        with dst_lmdb_env.begin() as txn:
+            logger.info(colored(f"no. of samples: {txn.stat()['entries']}", "cyan"))
+            n_total_filtered = 0
+            for type, n_filtered in n_filtered_out.items():
+                logger.info("{}: {}".format(type, n_filtered))
+                n_total_filtered += n_filtered
+            logger.info(colored("no. of excluded samples: {} ({:.1f}%)".format(
+                n_total_filtered, 100 * n_total_filtered / (txn.stat()["entries"] + n_total_filtered)), "cyan"))
+        dst_lmdb_env.sync()
+        dst_lmdb_env.close()
+    
+    def _sample_from_clip(
+        self, dst_lmdb_env, audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file,
+        vid_each_file, emo_each_file, sem_each_file,
+        disable_filtering, clean_first_seconds, clean_final_seconds, is_test,
+        ):
+        """
+        for data cleaning, we ignore the data for first and final n s
+        for test, we return all data 
+        """
+        # audio_start = int(self.alignment[0] * self.args.audio_fps)
+        # pose_start = int(self.alignment[1] * self.args.pose_fps)
+        #logger.info(f"before: {audio_each_file.shape} {pose_each_file.shape}")
+        # audio_each_file = audio_each_file[audio_start:]
+        # pose_each_file = pose_each_file[pose_start:]
+        # trans_each_file = 
+        #logger.info(f"after alignment: {audio_each_file.shape} {pose_each_file.shape}")
+        #print(pose_each_file.shape)
+        round_seconds_skeleton = pose_each_file.shape[0] // self.args.pose_fps  # assume 1500 frames / 15 fps = 100 s
+        #print(round_seconds_skeleton)
+        if audio_each_file != []:
+            if self.args.audio_rep != "wave16k":
+                round_seconds_audio = len(audio_each_file) // self.args.audio_fps # assume 16,000,00 / 16,000 = 100 s
+            elif self.args.audio_rep == "mfcc":
+                round_seconds_audio = audio_each_file.shape[0] // self.args.audio_fps
+            else:
+                round_seconds_audio = audio_each_file.shape[0] // self.args.audio_sr
+            if facial_each_file != []:
+                round_seconds_facial = facial_each_file.shape[0] // self.args.pose_fps
+                logger.info(f"audio: {round_seconds_audio}s, pose: {round_seconds_skeleton}s, facial: {round_seconds_facial}s")
+                round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton, round_seconds_facial)
+                max_round = max(round_seconds_audio, round_seconds_skeleton, round_seconds_facial)
+                if round_seconds_skeleton != max_round: 
+                    logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s")  
+            else:
+                logger.info(f"pose: {round_seconds_skeleton}s, audio: {round_seconds_audio}s")
+                round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton)
+                max_round = max(round_seconds_audio, round_seconds_skeleton)
+                if round_seconds_skeleton != max_round: 
+                    logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s")
+        
+        clip_s_t, clip_e_t = clean_first_seconds, round_seconds_skeleton - clean_final_seconds # assume [10, 90]s
+        clip_s_f_audio, clip_e_f_audio = self.args.audio_fps * clip_s_t, clip_e_t * self.args.audio_fps # [160,000,90*160,000]
+        clip_s_f_pose, clip_e_f_pose = clip_s_t * self.args.pose_fps, clip_e_t * self.args.pose_fps # [150,90*15]
+        
+        
+        for ratio in self.args.multi_length_training:
+            if is_test:# stride = length for test
+                cut_length = clip_e_f_pose - clip_s_f_pose
+                self.args.stride = cut_length
+                self.max_length = cut_length
+            else:
+                self.args.stride = int(ratio*self.ori_stride)
+                cut_length = int(self.ori_length*ratio)
+                
+            num_subdivision = math.floor((clip_e_f_pose - clip_s_f_pose - cut_length) / self.args.stride) + 1
+            logger.info(f"pose from frame {clip_s_f_pose} to {clip_e_f_pose}, length {cut_length}")
+            logger.info(f"{num_subdivision} clips is expected with stride {self.args.stride}")
+            
+            if audio_each_file != []:
+                audio_short_length = math.floor(cut_length / self.args.pose_fps * self.args.audio_fps)
+                """
+                for audio sr = 16000, fps = 15, pose_length = 34, 
+                audio short length = 36266.7 -> 36266
+                this error is fine.
+                """
+                logger.info(f"audio from frame {clip_s_f_audio} to {clip_e_f_audio}, length {audio_short_length}")
+             
+            n_filtered_out = defaultdict(int)
+            sample_pose_list = []
+            sample_audio_list = []
+            sample_facial_list = []
+            sample_shape_list = []
+            sample_word_list = []
+            sample_emo_list = []
+            sample_sem_list = []
+            sample_vid_list = []
+            sample_trans_list = []
+           
+            for i in range(num_subdivision): # cut into around 2s chip, (self npose)
+                start_idx = clip_s_f_pose + i * self.args.stride
+                fin_idx = start_idx + cut_length 
+                sample_pose = pose_each_file[start_idx:fin_idx]
+                sample_trans = trans_each_file[start_idx:fin_idx]
+                sample_shape = shape_each_file[start_idx:fin_idx]
+                # print(sample_pose.shape)
+                if self.args.audio_rep is not None:
+                    audio_start = clip_s_f_audio + math.floor(i * self.args.stride * self.args.audio_fps / self.args.pose_fps)
+                    audio_end = audio_start + audio_short_length
+                    sample_audio = audio_each_file[audio_start:audio_end]
+                else:
+                    sample_audio = np.array([-1])
+                sample_facial = facial_each_file[start_idx:fin_idx] if self.args.facial_rep is not None else np.array([-1])
+                sample_word = word_each_file[start_idx:fin_idx] if self.args.word_rep is not None else np.array([-1])
+                sample_emo = emo_each_file[start_idx:fin_idx] if self.args.emo_rep is not None else np.array([-1])
+                sample_sem = sem_each_file[start_idx:fin_idx] if self.args.sem_rep is not None else np.array([-1])
+                sample_vid = vid_each_file[start_idx:fin_idx] if self.args.id_rep is not None else np.array([-1])
+                
+                if sample_pose.any() != None:
+                    # filtering motion skeleton data
+                    sample_pose, filtering_message = MotionPreprocessor(sample_pose).get()
+                    is_correct_motion = (sample_pose != [])
+                    if is_correct_motion or disable_filtering:
+                        sample_pose_list.append(sample_pose)
+                        sample_audio_list.append(sample_audio)
+                        sample_facial_list.append(sample_facial)
+                        sample_shape_list.append(sample_shape)
+                        sample_word_list.append(sample_word)
+                        sample_vid_list.append(sample_vid)
+                        sample_emo_list.append(sample_emo)
+                        sample_sem_list.append(sample_sem)
+                        sample_trans_list.append(sample_trans)
+                    else:
+                        n_filtered_out[filtering_message] += 1
+
+            if len(sample_pose_list) > 0:
+                with dst_lmdb_env.begin(write=True) as txn:
+                    for pose, audio, facial, shape, word, vid, emo, sem, trans in zip(
+                        sample_pose_list,
+                        sample_audio_list,
+                        sample_facial_list,
+                        sample_shape_list,
+                        sample_word_list,
+                        sample_vid_list,
+                        sample_emo_list,
+                        sample_sem_list,
+                        sample_trans_list,):
+                        k = "{:005}".format(self.n_out_samples).encode("ascii")
+                        v = [pose, audio, facial, shape, word, emo, sem, vid, trans]
+                        v = pickle.dumps(v,5)
+                        txn.put(k, v)
+                        self.n_out_samples += 1
+        return n_filtered_out
+
+    def __getitem__(self, idx):
+        with self.lmdb_env.begin(write=False) as txn:
+            key = "{:005}".format(idx).encode("ascii")
+            sample = txn.get(key)
+            sample = pickle.loads(sample)
+            tar_pose, in_audio, in_facial, in_shape, in_word, emo, sem, vid, trans = sample
+            #print(in_shape)
+            #vid = torch.from_numpy(vid).int()
+            emo = torch.from_numpy(emo).int()
+            sem = torch.from_numpy(sem).float() 
+            in_audio = torch.from_numpy(in_audio).float() 
+            in_word = torch.from_numpy(in_word).float() if self.args.word_cache else torch.from_numpy(in_word).int() 
+            if self.loader_type == "test":
+                tar_pose = torch.from_numpy(tar_pose).float()
+                trans = torch.from_numpy(trans).float()
+                in_facial = torch.from_numpy(in_facial).float()
+                vid = torch.from_numpy(vid).float()
+                in_shape = torch.from_numpy(in_shape).float()
+            else:
+                in_shape = torch.from_numpy(in_shape).reshape((in_shape.shape[0], -1)).float()
+                trans = torch.from_numpy(trans).reshape((trans.shape[0], -1)).float()
+                vid = torch.from_numpy(vid).reshape((vid.shape[0], -1)).float()
+                tar_pose = torch.from_numpy(tar_pose).reshape((tar_pose.shape[0], -1)).float()
+                in_facial = torch.from_numpy(in_facial).reshape((in_facial.shape[0], -1)).float()
+            return {"pose":tar_pose, "audio":in_audio, "facial":in_facial, "beta": in_shape, "word":in_word, "id":vid, "emo":emo, "sem":sem, "trans":trans}
+
+         
+class MotionPreprocessor:
+    def __init__(self, skeletons):
+        self.skeletons = skeletons
+        #self.mean_pose = mean_pose
+        self.filtering_message = "PASS"
+
+    def get(self):
+        assert (self.skeletons is not None)
+
+        # filtering
+        if self.skeletons != []:
+            if self.check_pose_diff():
+                self.skeletons = []
+                self.filtering_message = "pose"
+            # elif self.check_spine_angle():
+            #     self.skeletons = []
+            #     self.filtering_message = "spine angle"
+            # elif self.check_static_motion():
+            #     self.skeletons = []
+            #     self.filtering_message = "motion"
+
+        # if self.skeletons != []:
+        #     self.skeletons = self.skeletons.tolist()
+        #     for i, frame in enumerate(self.skeletons):
+        #         assert not np.isnan(self.skeletons[i]).any()  # missing joints
+
+        return self.skeletons, self.filtering_message
+
+    def check_static_motion(self, verbose=True):
+        def get_variance(skeleton, joint_idx):
+            wrist_pos = skeleton[:, joint_idx]
+            variance = np.sum(np.var(wrist_pos, axis=0))
+            return variance
+
+        left_arm_var = get_variance(self.skeletons, 6)
+        right_arm_var = get_variance(self.skeletons, 9)
+
+        th = 0.0014  # exclude 13110
+        # th = 0.002  # exclude 16905
+        if left_arm_var < th and right_arm_var < th:
+            if verbose:
+                print("skip - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var))
+            return True
+        else:
+            if verbose:
+                print("pass - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var))
+            return False
+
+
+    def check_pose_diff(self, verbose=False):
+#         diff = np.abs(self.skeletons - self.mean_pose) # 186*1
+#         diff = np.mean(diff)
+
+#         # th = 0.017
+#         th = 0.02 #0.02  # exclude 3594
+#         if diff < th:
+#             if verbose:
+#                 print("skip - check_pose_diff {:.5f}".format(diff))
+#             return True
+# #         th = 3.5 #0.02  # exclude 3594
+# #         if 3.5 < diff < 5:
+# #             if verbose:
+# #                 print("skip - check_pose_diff {:.5f}".format(diff))
+# #             return True
+#         else:
+#             if verbose:
+#                 print("pass - check_pose_diff {:.5f}".format(diff))
+        return False
+
+
+    def check_spine_angle(self, verbose=True):
+        def angle_between(v1, v2):
+            v1_u = v1 / np.linalg.norm(v1)
+            v2_u = v2 / np.linalg.norm(v2)
+            return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0))
+
+        angles = []
+        for i in range(self.skeletons.shape[0]):
+            spine_vec = self.skeletons[i, 1] - self.skeletons[i, 0]
+            angle = angle_between(spine_vec, [0, -1, 0])
+            angles.append(angle)
+
+        if np.rad2deg(max(angles)) > 30 or np.rad2deg(np.mean(angles)) > 20:  # exclude 4495
+        # if np.rad2deg(max(angles)) > 20:  # exclude 8270
+            if verbose:
+                print("skip - check_spine_angle {:.5f}, {:.5f}".format(max(angles), np.mean(angles)))
+            return True
+        else:
+            if verbose:
+                print("pass - check_spine_angle {:.5f}".format(max(angles)))
+            return False

dataloaders/beat_sep_lower.py

@@ -0,0 +1,876 @@
+import os
+import pickle
+import math
+import shutil
+import numpy as np
+import lmdb as lmdb
+import textgrid as tg
+import pandas as pd
+import torch
+import glob
+import json
+from termcolor import colored
+from loguru import logger
+from collections import defaultdict
+from torch.utils.data import Dataset
+import torch.distributed as dist
+#import pyarrow
+import pickle
+import librosa
+import smplx
+
+from .build_vocab import Vocab
+from .utils.audio_features import Wav2Vec2Model
+from .data_tools import joints_list
+from .utils import rotation_conversions as rc
+from .utils import other_tools
+
+class CustomDataset(Dataset):
+    def __init__(self, args, loader_type, augmentation=None, kwargs=None, build_cache=True):
+        self.args = args
+        self.loader_type = loader_type
+
+        self.rank = dist.get_rank()
+        self.ori_stride = self.args.stride
+        self.ori_length = self.args.pose_length
+        self.alignment = [0,0] # for trinity
+        
+        self.ori_joint_list = joints_list[self.args.ori_joints]
+        self.tar_joint_list = joints_list[self.args.tar_joints]
+        if 'smplx' in self.args.pose_rep:
+            self.joint_mask = np.zeros(len(list(self.ori_joint_list.keys()))*3)
+            self.joints = len(list(self.tar_joint_list.keys()))  
+            for joint_name in self.tar_joint_list:
+                self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        else:
+            self.joints = len(list(self.ori_joint_list.keys()))+1
+            self.joint_mask = np.zeros(self.joints*3)
+            for joint_name in self.tar_joint_list:
+                if joint_name == "Hips":
+                    self.joint_mask[3:6] = 1
+                else:
+                    self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        # select trainable joints
+        self.smplx = smplx.create(
+            self.args.data_path_1+"smplx_models/", 
+            model_type='smplx',
+            gender='NEUTRAL_2020', 
+            use_face_contour=False,
+            num_betas=300,
+            num_expression_coeffs=100, 
+            ext='npz',
+            use_pca=False,
+        ).cuda().eval()
+
+        split_rule = pd.read_csv(args.data_path+"train_test_split.csv")
+        self.selected_file = split_rule.loc[(split_rule['type'] == loader_type) & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+        if args.additional_data and loader_type == 'train':
+            split_b = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            #self.selected_file = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            self.selected_file = pd.concat([self.selected_file, split_b])
+        if self.selected_file.empty:
+            logger.warning(f"{loader_type} is empty for speaker {self.args.training_speakers}, use train set 0-8 instead")
+            self.selected_file = split_rule.loc[(split_rule['type'] == 'train') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            self.selected_file = self.selected_file.iloc[0:8]
+        self.data_dir = args.data_path 
+        
+        if loader_type == "test": 
+            self.args.multi_length_training = [1.0]
+        self.max_length = int(args.pose_length * self.args.multi_length_training[-1])
+        self.max_audio_pre_len = math.floor(args.pose_length / args.pose_fps * self.args.audio_sr)
+        if self.max_audio_pre_len > self.args.test_length*self.args.audio_sr: 
+            self.max_audio_pre_len = self.args.test_length*self.args.audio_sr
+        
+        if args.word_rep is not None:
+            with open(f"{args.data_path}weights/vocab.pkl", 'rb') as f:
+                self.lang_model = pickle.load(f)
+                
+        preloaded_dir = self.args.root_path + self.args.cache_path + loader_type + f"/{args.pose_rep}_cache"      
+        # if args.pose_norm:
+        #     # careful for rotation vectors
+        #     if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy"):
+        #         self.calculate_mean_pose()
+        #     self.mean_pose = np.load(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy")
+        #     self.std_pose = np.load(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_std.npy")
+        # if args.audio_norm:
+        #     if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/bvh_mean.npy"):
+        #         self.calculate_mean_audio()
+        #     self.mean_audio = np.load(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/npy_mean.npy")
+        #     self.std_audio = np.load(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/npy_std.npy")
+        # if args.facial_norm:
+        #     if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy"):
+        #         self.calculate_mean_face()
+        #     self.mean_facial = np.load(args.data_path+args.mean_pose_path+f"{args.facial_rep}/json_mean.npy")
+        #     self.std_facial = np.load(args.data_path+args.mean_pose_path+f"{args.facial_rep}/json_std.npy")
+        if self.args.beat_align:
+            if not os.path.exists(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy"):
+                self.calculate_mean_velocity(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy")
+            self.avg_vel = np.load(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy")
+            
+        if build_cache and self.rank == 0:
+            self.build_cache(preloaded_dir)
+        self.lmdb_env = lmdb.open(preloaded_dir, readonly=True, lock=False)
+        with self.lmdb_env.begin() as txn:
+            self.n_samples = txn.stat()["entries"] 
+
+    
+    def calculate_mean_velocity(self, save_path):
+        self.smplx = smplx.create(
+            self.args.data_path_1+"smplx_models/", 
+            model_type='smplx',
+            gender='NEUTRAL_2020', 
+            use_face_contour=False,
+            num_betas=300,
+            num_expression_coeffs=100, 
+            ext='npz',
+            use_pca=False,
+        ).cuda().eval()
+        dir_p = self.data_dir + self.args.pose_rep + "/"
+        all_list = []
+        from tqdm import tqdm
+        for tar in tqdm(os.listdir(dir_p)):
+            if tar.endswith(".npz"):
+                m_data = np.load(dir_p+tar, allow_pickle=True)
+                betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"]
+                n, c = poses.shape[0], poses.shape[1]
+                betas = betas.reshape(1, 300)
+                betas = np.tile(betas, (n, 1))
+                betas = torch.from_numpy(betas).cuda().float()
+                poses = torch.from_numpy(poses.reshape(n, c)).cuda().float()
+                exps = torch.from_numpy(exps.reshape(n, 100)).cuda().float()
+                trans = torch.from_numpy(trans.reshape(n, 3)).cuda().float()
+                max_length = 128
+                s, r = n//max_length, n%max_length
+                #print(n, s, r)
+                all_tensor = []
+                for i in range(s):
+                    with torch.no_grad():
+                        joints = self.smplx(
+                            betas=betas[i*max_length:(i+1)*max_length], 
+                            transl=trans[i*max_length:(i+1)*max_length], 
+                            expression=exps[i*max_length:(i+1)*max_length], 
+                            jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], 
+                            global_orient=poses[i*max_length:(i+1)*max_length,:3], 
+                            body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], 
+                            left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], 
+                            right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], 
+                            return_verts=True,
+                            return_joints=True,
+                            leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], 
+                            reye_pose=poses[i*max_length:(i+1)*max_length, 72:75],
+                        )['joints'][:, :55, :].reshape(max_length, 55*3)
+                    all_tensor.append(joints)
+                if r != 0:
+                    with torch.no_grad():
+                        joints = self.smplx(
+                            betas=betas[s*max_length:s*max_length+r], 
+                            transl=trans[s*max_length:s*max_length+r], 
+                            expression=exps[s*max_length:s*max_length+r], 
+                            jaw_pose=poses[s*max_length:s*max_length+r, 66:69], 
+                            global_orient=poses[s*max_length:s*max_length+r,:3], 
+                            body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], 
+                            left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], 
+                            right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], 
+                            return_verts=True,
+                            return_joints=True,
+                            leye_pose=poses[s*max_length:s*max_length+r, 69:72], 
+                            reye_pose=poses[s*max_length:s*max_length+r, 72:75],
+                        )['joints'][:, :55, :].reshape(r, 55*3)
+                    all_tensor.append(joints)
+                joints = torch.cat(all_tensor, axis=0)
+                joints = joints.permute(1, 0)
+                dt = 1/30
+            # first steps is forward diff (t+1 - t) / dt
+                init_vel = (joints[:, 1:2] - joints[:, :1]) / dt
+                # middle steps are second order (t+1 - t-1) / 2dt
+                middle_vel = (joints[:, 2:] - joints[:, 0:-2]) / (2 * dt)
+                # last step is backward diff (t - t-1) / dt
+                final_vel = (joints[:, -1:] - joints[:, -2:-1]) / dt
+                #print(joints.shape, init_vel.shape, middle_vel.shape, final_vel.shape)
+                vel_seq = torch.cat([init_vel, middle_vel, final_vel], dim=1).permute(1, 0).reshape(n, 55, 3)
+                #print(vel_seq.shape)
+                #.permute(1, 0).reshape(n, 55, 3)
+                vel_seq_np = vel_seq.cpu().numpy()
+                vel_joints_np = np.linalg.norm(vel_seq_np, axis=2) # n * 55
+                all_list.append(vel_joints_np)
+        avg_vel = np.mean(np.concatenate(all_list, axis=0),axis=0) # 55
+        np.save(save_path, avg_vel)
+        
+    
+    def build_cache(self, preloaded_dir):
+        logger.info(f"Audio bit rate: {self.args.audio_fps}")
+        logger.info("Reading data '{}'...".format(self.data_dir))
+        logger.info("Creating the dataset cache...")
+        if self.args.new_cache:
+            if os.path.exists(preloaded_dir):
+                shutil.rmtree(preloaded_dir)
+        if os.path.exists(preloaded_dir):
+            logger.info("Found the cache {}".format(preloaded_dir))
+        elif self.loader_type == "test":
+            self.cache_generation(
+                preloaded_dir, True, 
+                0, 0,
+                is_test=True)
+        else:
+            self.cache_generation(
+                preloaded_dir, self.args.disable_filtering, 
+                self.args.clean_first_seconds, self.args.clean_final_seconds,
+                is_test=False)
+        
+    def __len__(self):
+        return self.n_samples
+    
+
+    def cache_generation(self, out_lmdb_dir, disable_filtering, clean_first_seconds,  clean_final_seconds, is_test=False):
+        # if "wav2vec2" in self.args.audio_rep:
+        #     self.wav2vec_model = Wav2Vec2Model.from_pretrained(f"{self.args.data_path_1}/hub/transformer/wav2vec2-base-960h")
+        #     self.wav2vec_model.feature_extractor._freeze_parameters()
+        #     self.wav2vec_model = self.wav2vec_model.cuda()
+        #     self.wav2vec_model.eval()
+        
+        self.n_out_samples = 0
+        # create db for samples
+        if not os.path.exists(out_lmdb_dir): os.makedirs(out_lmdb_dir)
+        dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 500))# 500G
+        n_filtered_out = defaultdict(int)
+    
+        for index, file_name in self.selected_file.iterrows():
+            f_name = file_name["id"]
+            ext = ".npz" if "smplx" in self.args.pose_rep else ".bvh"
+            pose_file = self.data_dir + self.args.pose_rep + "/" + f_name + ext
+            pose_each_file = []
+            trans_each_file = []
+            trans_v_each_file = []
+            shape_each_file = []
+            audio_each_file = []
+            facial_each_file = []
+            word_each_file = []
+            emo_each_file = []
+            sem_each_file = []
+            vid_each_file = []
+            id_pose = f_name #1_wayne_0_1_1
+            
+            logger.info(colored(f"# ---- Building cache for Pose   {id_pose} ---- #", "blue"))
+            if "smplx" in self.args.pose_rep:
+                pose_data = np.load(pose_file, allow_pickle=True)
+                assert 30%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 30'
+                stride = int(30/self.args.pose_fps)
+                pose_each_file = pose_data["poses"][::stride] 
+                trans_each_file = pose_data["trans"][::stride]
+                trans_each_file[:,0] = trans_each_file[:,0] - trans_each_file[0,0]
+                trans_each_file[:,2] = trans_each_file[:,2] - trans_each_file[0,2]
+                trans_v_each_file = np.zeros_like(trans_each_file)
+                trans_v_each_file[1:,0] = trans_each_file[1:,0] - trans_each_file[:-1,0]
+                trans_v_each_file[0,0] = trans_v_each_file[1,0]
+                trans_v_each_file[1:,2] = trans_each_file[1:,2] - trans_each_file[:-1,2]
+                trans_v_each_file[0,2] = trans_v_each_file[1,2]
+                trans_v_each_file[:,1] = trans_each_file[:,1]
+                shape_each_file = np.repeat(pose_data["betas"].reshape(1, 300), pose_each_file.shape[0], axis=0)
+                
+                assert self.args.pose_fps == 30, "should 30"
+                m_data = np.load(pose_file, allow_pickle=True)
+                betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"]
+                n, c = poses.shape[0], poses.shape[1]
+                betas = betas.reshape(1, 300)
+                betas = np.tile(betas, (n, 1))
+                betas = torch.from_numpy(betas).cuda().float()
+                poses = torch.from_numpy(poses.reshape(n, c)).cuda().float()
+                exps = torch.from_numpy(exps.reshape(n, 100)).cuda().float()
+                trans = torch.from_numpy(trans.reshape(n, 3)).cuda().float()
+                max_length = 128    # 为什么这里需要一��max_length
+                s, r = n//max_length, n%max_length
+                #print(n, s, r)
+                all_tensor = []
+                for i in range(s):
+                    with torch.no_grad():
+                        joints = self.smplx(
+                            betas=betas[i*max_length:(i+1)*max_length], 
+                            transl=trans[i*max_length:(i+1)*max_length], 
+                            expression=exps[i*max_length:(i+1)*max_length], 
+                            jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], 
+                            global_orient=poses[i*max_length:(i+1)*max_length,:3], 
+                            body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], 
+                            left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], 
+                            right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], 
+                            return_verts=True,
+                            return_joints=True,
+                            leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], 
+                            reye_pose=poses[i*max_length:(i+1)*max_length, 72:75],
+                        )['joints'][:, (7,8,10,11), :].reshape(max_length, 4, 3).cpu()
+                    all_tensor.append(joints)
+                if r != 0:
+                    with torch.no_grad():
+                        joints = self.smplx(
+                            betas=betas[s*max_length:s*max_length+r], 
+                            transl=trans[s*max_length:s*max_length+r], 
+                            expression=exps[s*max_length:s*max_length+r], 
+                            jaw_pose=poses[s*max_length:s*max_length+r, 66:69], 
+                            global_orient=poses[s*max_length:s*max_length+r,:3], 
+                            body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], 
+                            left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], 
+                            right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], 
+                            return_verts=True,
+                            return_joints=True,
+                            leye_pose=poses[s*max_length:s*max_length+r, 69:72], 
+                            reye_pose=poses[s*max_length:s*max_length+r, 72:75],
+                        )['joints'][:, (7,8,10,11), :].reshape(r, 4, 3).cpu()
+                    all_tensor.append(joints)
+                joints = torch.cat(all_tensor, axis=0) # all, 4, 3
+                # print(joints.shape)
+                feetv = torch.zeros(joints.shape[1], joints.shape[0])
+                joints = joints.permute(1, 0, 2)
+                #print(joints.shape, feetv.shape)
+                feetv[:, :-1] = (joints[:, 1:] - joints[:, :-1]).norm(dim=-1)
+                #print(feetv.shape)
+                contacts = (feetv < 0.01).numpy().astype(float)
+                # print(contacts.shape, contacts)
+                contacts = contacts.transpose(1, 0)
+                pose_each_file = pose_each_file * self.joint_mask
+                pose_each_file = pose_each_file[:, self.joint_mask.astype(bool)]
+                pose_each_file = np.concatenate([pose_each_file, contacts], axis=1)
+                # print(pose_each_file.shape)
+                
+                
+                if self.args.facial_rep is not None:
+                    logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #")
+                    facial_each_file = pose_data["expressions"][::stride]
+                    if self.args.facial_norm: 
+                        facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial
+                    
+            else:
+                assert 120%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120'
+                stride = int(120/self.args.pose_fps)
+                with open(pose_file, "r") as pose_data:
+                    for j, line in enumerate(pose_data.readlines()):
+                        if j < 431: continue     
+                        if j%stride != 0:continue
+                        data = np.fromstring(line, dtype=float, sep=" ")
+                        rot_data = rc.euler_angles_to_matrix(torch.from_numpy(np.deg2rad(data)).reshape(-1, self.joints,3), "XYZ")
+                        rot_data = rc.matrix_to_axis_angle(rot_data).reshape(-1, self.joints*3) 
+                        rot_data = rot_data.numpy() * self.joint_mask
+                        
+                        pose_each_file.append(rot_data)
+                        trans_each_file.append(data[:3])
+                        
+                pose_each_file = np.array(pose_each_file)
+                # print(pose_each_file.shape)
+                trans_each_file = np.array(trans_each_file)
+                shape_each_file = np.repeat(np.array(-1).reshape(1, 1), pose_each_file.shape[0], axis=0)
+                if self.args.facial_rep is not None:
+                    logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #")
+                    facial_file = pose_file.replace(self.args.pose_rep, self.args.facial_rep).replace("bvh", "json")
+                    assert 60%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120'
+                    stride = int(60/self.args.pose_fps)
+                    if not os.path.exists(facial_file):
+                        logger.warning(f"# ---- file not found for Facial {id_pose}, skip all files with the same id ---- #")
+                        self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index)
+                        continue
+                    with open(facial_file, 'r') as facial_data_file:
+                        facial_data = json.load(facial_data_file)
+                        for j, frame_data in enumerate(facial_data['frames']):
+                            if j%stride != 0:continue
+                            facial_each_file.append(frame_data['weights'])
+                    facial_each_file = np.array(facial_each_file)
+                    if self.args.facial_norm: 
+                        facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial
+                        
+            if self.args.id_rep is not None:
+                vid_each_file = np.repeat(np.array(int(f_name.split("_")[0])-1).reshape(1, 1), pose_each_file.shape[0], axis=0)
+      
+            if self.args.audio_rep is not None:
+                logger.info(f"# ---- Building cache for Audio  {id_pose} and Pose {id_pose} ---- #")
+                audio_file = pose_file.replace(self.args.pose_rep, 'wave16k').replace(ext, ".wav")
+                if not os.path.exists(audio_file):
+                    logger.warning(f"# ---- file not found for Audio  {id_pose}, skip all files with the same id ---- #")
+                    self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index)
+                    continue
+                audio_save_path = audio_file.replace("wave16k", "onset_amplitude").replace(".wav", ".npy")
+                if self.args.audio_rep == "onset+amplitude" and os.path.exists(audio_save_path):
+                    audio_each_file = np.load(audio_save_path)
+                    logger.warning(f"# ---- file found cache for Audio   {id_pose}   ---- #")
+                elif self.args.audio_rep == "onset+amplitude":
+                    audio_each_file, sr = librosa.load(audio_file)
+                    audio_each_file = librosa.resample(audio_each_file, orig_sr=sr, target_sr=self.args.audio_sr)
+                    from numpy.lib import stride_tricks
+                    frame_length = 1024
+                    # hop_length = 512
+                    shape = (audio_each_file.shape[-1] - frame_length + 1, frame_length)
+                    strides = (audio_each_file.strides[-1], audio_each_file.strides[-1])
+                    rolling_view = stride_tricks.as_strided(audio_each_file, shape=shape, strides=strides)
+                    amplitude_envelope = np.max(np.abs(rolling_view), axis=1)
+                    # pad the last frame_length-1 samples
+                    amplitude_envelope = np.pad(amplitude_envelope, (0, frame_length-1), mode='constant', constant_values=amplitude_envelope[-1])
+                    audio_onset_f = librosa.onset.onset_detect(y=audio_each_file, sr=self.args.audio_sr, units='frames')
+                    onset_array = np.zeros(len(audio_each_file), dtype=float)
+                    onset_array[audio_onset_f] = 1.0
+                    # print(amplitude_envelope.shape, audio_each_file.shape, onset_array.shape)
+                    audio_each_file = np.concatenate([amplitude_envelope.reshape(-1, 1), onset_array.reshape(-1, 1)], axis=1)
+                    audio_save_path = audio_file.replace("wave16k", "onset_amplitude").replace(".wav", ".npy")
+                    np.save(audio_save_path, audio_each_file)
+                    
+                elif self.args.audio_rep == "mfcc":
+                    audio_each_file = librosa.feature.melspectrogram(y=audio_each_file, sr=self.args.audio_sr, n_mels=128, hop_length=int(self.args.audio_sr/self.args.audio_fps))
+                    audio_each_file = audio_each_file.transpose(1, 0)
+                    # print(audio_each_file.shape, pose_each_file.shape)
+                if self.args.audio_norm and self.args.audio_rep == "wave16k": 
+                    audio_each_file = (audio_each_file - self.mean_audio) / self.std_audio
+                    
+            time_offset = 0
+            if self.args.word_rep is not None:
+                logger.info(f"# ---- Building cache for Word   {id_pose} and Pose {id_pose} ---- #")
+                word_file = f"{self.data_dir}{self.args.word_rep}/{id_pose}.TextGrid"
+                if not os.path.exists(word_file):
+                    logger.warning(f"# ---- file not found for Word   {id_pose}, skip all files with the same id ---- #")
+                    self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index)
+                    continue
+                word_save_path = f"{self.data_dir}{self.args.t_pre_encoder}/{id_pose}.npy"
+                if os.path.exists(word_save_path):
+                    word_each_file = np.load(word_save_path)
+                    logger.warning(f"# ---- file found cache for Word   {id_pose}   ---- #")
+                else:
+                    tgrid = tg.TextGrid.fromFile(word_file)
+                    if self.args.t_pre_encoder == "bert":
+                        from transformers import AutoTokenizer, BertModel
+                        tokenizer = AutoTokenizer.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True)
+                        model = BertModel.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True).eval()
+                        list_word = []
+                        all_hidden = []
+                        max_len = 400
+                        last = 0
+                        word_token_mapping = []
+                        first = True
+                        for i, word in enumerate(tgrid[0]):
+                            last = i
+                            if (i%max_len != 0) or (i==0):
+                                if word.mark == "":
+                                    list_word.append(".")
+                                else:
+                                    list_word.append(word.mark)
+                            else:
+                                max_counter = max_len
+                                str_word = ' '.join(map(str, list_word))
+                                if first:
+                                    global_len = 0
+                                end = -1
+                                offset_word = []
+                                for k, wordvalue in enumerate(list_word):
+                                    start = end+1 
+                                    end = start+len(wordvalue)
+                                    offset_word.append((start, end))
+                                #print(offset_word)
+                                token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping']
+                                #print(token_scan)
+                                for start, end in offset_word:
+                                    sub_mapping = []
+                                    for i, (start_t, end_t) in enumerate(token_scan[1:-1]):
+                                        if int(start) <= int(start_t) and int(end_t) <= int(end):
+                                            #print(i+global_len)
+                                            sub_mapping.append(i+global_len)
+                                    word_token_mapping.append(sub_mapping)
+                                #print(len(word_token_mapping))
+                                global_len = word_token_mapping[-1][-1] + 1    
+                                list_word = []
+                                if word.mark == "":
+                                    list_word.append(".")
+                                else:
+                                    list_word.append(word.mark)
+                                
+                                with torch.no_grad():
+                                    inputs = tokenizer(str_word, return_tensors="pt")
+                                    outputs = model(**inputs)
+                                    last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :]
+                                all_hidden.append(last_hidden_states)
+                        
+                        #list_word = list_word[:10]
+                        if list_word == []:
+                            pass
+                        else:
+                            if first: 
+                                global_len = 0
+                            str_word = ' '.join(map(str, list_word))
+                            end = -1
+                            offset_word = []
+                            for k, wordvalue in enumerate(list_word):
+                                start = end+1 
+                                end = start+len(wordvalue)
+                                offset_word.append((start, end))
+                            #print(offset_word)
+                            token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping']
+                            #print(token_scan)
+                            for start, end in offset_word:
+                                sub_mapping = []
+                                for i, (start_t, end_t) in enumerate(token_scan[1:-1]):
+                                    if int(start) <= int(start_t) and int(end_t) <= int(end):
+                                        sub_mapping.append(i+global_len)
+                                        #print(sub_mapping)
+                                word_token_mapping.append(sub_mapping)
+                            #print(len(word_token_mapping))
+                            with torch.no_grad():
+                                inputs = tokenizer(str_word, return_tensors="pt")
+                                outputs = model(**inputs)
+                                last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :]
+                            all_hidden.append(last_hidden_states)
+                        last_hidden_states = np.concatenate(all_hidden, axis=0)
+                
+                    for i in range(pose_each_file.shape[0]):
+                        found_flag = False
+                        current_time = i/self.args.pose_fps + time_offset
+                        j_last = 0
+                        for j, word in enumerate(tgrid[0]): 
+                            word_n, word_s, word_e = word.mark, word.minTime, word.maxTime
+                            if word_s<=current_time and current_time<=word_e:
+                                if self.args.word_cache and self.args.t_pre_encoder == 'bert':
+                                    mapping_index = word_token_mapping[j]
+                                    #print(mapping_index, word_s, word_e)
+                                    s_t = np.linspace(word_s, word_e, len(mapping_index)+1)
+                                    #print(s_t)
+                                    for tt, t_sep in enumerate(s_t[1:]):
+                                        if current_time <= t_sep:
+                                            #if len(mapping_index) > 1: print(mapping_index[tt])
+                                            word_each_file.append(last_hidden_states[mapping_index[tt]])
+                                            break
+                                else:
+                                    if word_n == " ":
+                                        word_each_file.append(self.lang_model.PAD_token)
+                                    else:
+                                        word_each_file.append(self.lang_model.get_word_index(word_n))
+                                found_flag = True
+                                j_last = j
+                                break
+                            else: continue   
+                        if not found_flag: 
+                            if self.args.word_cache and self.args.t_pre_encoder == 'bert':
+                                word_each_file.append(last_hidden_states[j_last])
+                            else:
+                                word_each_file.append(self.lang_model.UNK_token)
+                    word_each_file = np.array(word_each_file)
+                    word_save_path = f"{self.data_dir}{self.args.t_pre_encoder}/{id_pose}.npy"
+                    np.save(word_save_path, word_each_file)
+                    #print(word_each_file.shape)
+                #print(word_each_file.shape)
+                
+            if self.args.emo_rep is not None:
+                logger.info(f"# ---- Building cache for Emo    {id_pose} and Pose {id_pose} ---- #")
+                rtype, start = int(id_pose.split('_')[3]), int(id_pose.split('_')[3])
+                if rtype == 0 or rtype == 2 or rtype == 4 or rtype == 6:
+                    if start >= 1 and start <= 64:
+                        score = 0
+                    elif start >= 65 and start <= 72:
+                        score = 1
+                    elif start >= 73 and start <= 80:
+                        score = 2
+                    elif start >= 81 and start <= 86:
+                        score = 3
+                    elif start >= 87 and start <= 94:
+                        score = 4
+                    elif start >= 95 and start <= 102:
+                        score = 5
+                    elif start >= 103 and start <= 110:
+                        score = 6
+                    elif start >= 111 and start <= 118:
+                        score = 7
+                    else: pass
+                else:
+                    # you may denote as unknown in the future
+                    score = 0
+                emo_each_file = np.repeat(np.array(score).reshape(1, 1), pose_each_file.shape[0], axis=0)    
+                #print(emo_each_file)
+                
+            if self.args.sem_rep is not None:
+                logger.info(f"# ---- Building cache for Sem    {id_pose} and Pose {id_pose} ---- #")
+                sem_file = f"{self.data_dir}{self.args.sem_rep}/{id_pose}.txt" 
+                sem_all = pd.read_csv(sem_file, 
+                    sep='\t', 
+                    names=["name", "start_time", "end_time", "duration", "score", "keywords"])
+                # we adopt motion-level semantic score here. 
+                for i in range(pose_each_file.shape[0]):
+                    found_flag = False
+                    for j, (start, end, score) in enumerate(zip(sem_all['start_time'],sem_all['end_time'], sem_all['score'])):
+                        current_time = i/self.args.pose_fps + time_offset
+                        if start<=current_time and current_time<=end: 
+                            sem_each_file.append(score)
+                            found_flag=True
+                            break
+                        else: continue 
+                    if not found_flag: sem_each_file.append(0.)
+                sem_each_file = np.array(sem_each_file)
+                #print(sem_each_file)
+            
+            filtered_result = self._sample_from_clip(
+                dst_lmdb_env,
+                audio_each_file, pose_each_file, trans_each_file, trans_v_each_file,shape_each_file, facial_each_file, word_each_file,
+                vid_each_file, emo_each_file, sem_each_file,
+                disable_filtering, clean_first_seconds, clean_final_seconds, is_test,
+                ) 
+            for type in filtered_result.keys():
+                n_filtered_out[type] += filtered_result[type]
+                                
+        with dst_lmdb_env.begin() as txn:
+            logger.info(colored(f"no. of samples: {txn.stat()['entries']}", "cyan"))
+            n_total_filtered = 0
+            for type, n_filtered in n_filtered_out.items():
+                logger.info("{}: {}".format(type, n_filtered))
+                n_total_filtered += n_filtered
+            logger.info(colored("no. of excluded samples: {} ({:.1f}%)".format(
+                n_total_filtered, 100 * n_total_filtered / (txn.stat()["entries"] + n_total_filtered)), "cyan"))
+        dst_lmdb_env.sync()
+        dst_lmdb_env.close()
+    
+    def _sample_from_clip(
+        self, dst_lmdb_env, audio_each_file, pose_each_file, trans_each_file, trans_v_each_file,shape_each_file, facial_each_file, word_each_file,
+        vid_each_file, emo_each_file, sem_each_file,
+        disable_filtering, clean_first_seconds, clean_final_seconds, is_test,
+        ):
+        """
+        for data cleaning, we ignore the data for first and final n s
+        for test, we return all data 
+        """
+        # audio_start = int(self.alignment[0] * self.args.audio_fps)
+        # pose_start = int(self.alignment[1] * self.args.pose_fps)
+        #logger.info(f"before: {audio_each_file.shape} {pose_each_file.shape}")
+        # audio_each_file = audio_each_file[audio_start:]
+        # pose_each_file = pose_each_file[pose_start:]
+        # trans_each_file = 
+        #logger.info(f"after alignment: {audio_each_file.shape} {pose_each_file.shape}")
+        #print(pose_each_file.shape)
+        round_seconds_skeleton = pose_each_file.shape[0] // self.args.pose_fps  # assume 1500 frames / 15 fps = 100 s
+        #print(round_seconds_skeleton)
+        if audio_each_file != []:
+            if self.args.audio_rep != "wave16k":
+                round_seconds_audio = len(audio_each_file) // self.args.audio_fps # assume 16,000,00 / 16,000 = 100 s
+            elif self.args.audio_rep == "mfcc":
+                round_seconds_audio = audio_each_file.shape[0] // self.args.audio_fps
+            else:
+                round_seconds_audio = audio_each_file.shape[0] // self.args.audio_sr
+            if facial_each_file != []:
+                round_seconds_facial = facial_each_file.shape[0] // self.args.pose_fps
+                logger.info(f"audio: {round_seconds_audio}s, pose: {round_seconds_skeleton}s, facial: {round_seconds_facial}s")
+                round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton, round_seconds_facial)
+                max_round = max(round_seconds_audio, round_seconds_skeleton, round_seconds_facial)
+                if round_seconds_skeleton != max_round: 
+                    logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s")  
+            else:
+                logger.info(f"pose: {round_seconds_skeleton}s, audio: {round_seconds_audio}s")
+                round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton)
+                max_round = max(round_seconds_audio, round_seconds_skeleton)
+                if round_seconds_skeleton != max_round: 
+                    logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s")
+        
+        clip_s_t, clip_e_t = clean_first_seconds, round_seconds_skeleton - clean_final_seconds # assume [10, 90]s
+        clip_s_f_audio, clip_e_f_audio = self.args.audio_fps * clip_s_t, clip_e_t * self.args.audio_fps # [160,000,90*160,000]
+        clip_s_f_pose, clip_e_f_pose = clip_s_t * self.args.pose_fps, clip_e_t * self.args.pose_fps # [150,90*15]
+        
+        
+        for ratio in self.args.multi_length_training:
+            if is_test:# stride = length for test
+                cut_length = clip_e_f_pose - clip_s_f_pose
+                self.args.stride = cut_length
+                self.max_length = cut_length
+            else:
+                self.args.stride = int(ratio*self.ori_stride)
+                cut_length = int(self.ori_length*ratio)
+                
+            num_subdivision = math.floor((clip_e_f_pose - clip_s_f_pose - cut_length) / self.args.stride) + 1
+            logger.info(f"pose from frame {clip_s_f_pose} to {clip_e_f_pose}, length {cut_length}")
+            logger.info(f"{num_subdivision} clips is expected with stride {self.args.stride}")
+            
+            if audio_each_file != []:
+                audio_short_length = math.floor(cut_length / self.args.pose_fps * self.args.audio_fps)
+                """
+                for audio sr = 16000, fps = 15, pose_length = 34, 
+                audio short length = 36266.7 -> 36266
+                this error is fine.
+                """
+                logger.info(f"audio from frame {clip_s_f_audio} to {clip_e_f_audio}, length {audio_short_length}")
+             
+            n_filtered_out = defaultdict(int)
+            sample_pose_list = []
+            sample_audio_list = []
+            sample_facial_list = []
+            sample_shape_list = []
+            sample_word_list = []
+            sample_emo_list = []
+            sample_sem_list = []
+            sample_vid_list = []
+            sample_trans_list = []
+            sample_trans_v_list = []
+           
+            for i in range(num_subdivision): # cut into around 2s chip, (self npose)
+                start_idx = clip_s_f_pose + i * self.args.stride
+                fin_idx = start_idx + cut_length 
+                sample_pose = pose_each_file[start_idx:fin_idx]
+
+                sample_trans = trans_each_file[start_idx:fin_idx]
+                sample_trans_v = trans_v_each_file[start_idx:fin_idx]
+                sample_shape = shape_each_file[start_idx:fin_idx]
+                # print(sample_pose.shape)
+                if self.args.audio_rep is not None:
+                    audio_start = clip_s_f_audio + math.floor(i * self.args.stride * self.args.audio_fps / self.args.pose_fps)
+                    audio_end = audio_start + audio_short_length
+                    sample_audio = audio_each_file[audio_start:audio_end]
+                else:
+                    sample_audio = np.array([-1])
+                sample_facial = facial_each_file[start_idx:fin_idx] if self.args.facial_rep is not None else np.array([-1])
+                sample_word = word_each_file[start_idx:fin_idx] if self.args.word_rep is not None else np.array([-1])
+                sample_emo = emo_each_file[start_idx:fin_idx] if self.args.emo_rep is not None else np.array([-1])
+                sample_sem = sem_each_file[start_idx:fin_idx] if self.args.sem_rep is not None else np.array([-1])
+                sample_vid = vid_each_file[start_idx:fin_idx] if self.args.id_rep is not None else np.array([-1])
+                
+                if sample_pose.any() != None:
+                    # filtering motion skeleton data
+                    sample_pose, filtering_message = MotionPreprocessor(sample_pose).get()
+                    is_correct_motion = (sample_pose != [])
+                    if is_correct_motion or disable_filtering:
+                        sample_pose_list.append(sample_pose)
+                        sample_audio_list.append(sample_audio)
+                        sample_facial_list.append(sample_facial)
+                        sample_shape_list.append(sample_shape)
+                        sample_word_list.append(sample_word)
+                        sample_vid_list.append(sample_vid)
+                        sample_emo_list.append(sample_emo)
+                        sample_sem_list.append(sample_sem)
+                        sample_trans_list.append(sample_trans)
+                        sample_trans_v_list.append(sample_trans_v)
+                    else:
+                        n_filtered_out[filtering_message] += 1
+
+            if len(sample_pose_list) > 0:
+                with dst_lmdb_env.begin(write=True) as txn:
+                    for pose, audio, facial, shape, word, vid, emo, sem, trans,trans_v in zip(
+                        sample_pose_list,
+                        sample_audio_list,
+                        sample_facial_list,
+                        sample_shape_list,
+                        sample_word_list,
+                        sample_vid_list,
+                        sample_emo_list,
+                        sample_sem_list,
+                        sample_trans_list,
+                        sample_trans_v_list,):
+                        k = "{:005}".format(self.n_out_samples).encode("ascii")
+                        v = [pose, audio, facial, shape, word, emo, sem, vid, trans,trans_v]
+                        v = pickle.dumps(v,5)
+                        txn.put(k, v)
+                        self.n_out_samples += 1
+        return n_filtered_out
+
+    def __getitem__(self, idx):
+        with self.lmdb_env.begin(write=False) as txn:
+            key = "{:005}".format(idx).encode("ascii")
+            sample = txn.get(key)
+            sample = pickle.loads(sample)
+            tar_pose, in_audio, in_facial, in_shape, in_word, emo, sem, vid, trans,trans_v = sample
+            #print(in_shape)
+            #vid = torch.from_numpy(vid).int()
+            emo = torch.from_numpy(emo).int()
+            sem = torch.from_numpy(sem).float() 
+            in_audio = torch.from_numpy(in_audio).float() 
+            in_word = torch.from_numpy(in_word).float() if self.args.word_cache else torch.from_numpy(in_word).int() 
+            if self.loader_type == "test":
+                tar_pose = torch.from_numpy(tar_pose).float()
+                trans = torch.from_numpy(trans).float()
+                trans_v = torch.from_numpy(trans_v).float()
+                in_facial = torch.from_numpy(in_facial).float()
+                vid = torch.from_numpy(vid).float()
+                in_shape = torch.from_numpy(in_shape).float()
+            else:
+                in_shape = torch.from_numpy(in_shape).reshape((in_shape.shape[0], -1)).float()
+                trans = torch.from_numpy(trans).reshape((trans.shape[0], -1)).float()
+                trans_v = torch.from_numpy(trans_v).reshape((trans_v.shape[0], -1)).float()
+                vid = torch.from_numpy(vid).reshape((vid.shape[0], -1)).float()
+                tar_pose = torch.from_numpy(tar_pose).reshape((tar_pose.shape[0], -1)).float()
+                in_facial = torch.from_numpy(in_facial).reshape((in_facial.shape[0], -1)).float()
+            return {"pose":tar_pose, "audio":in_audio, "facial":in_facial, "beta": in_shape, "word":in_word, "id":vid, "emo":emo, "sem":sem, "trans":trans,"trans_v":trans_v}
+
+         
+class MotionPreprocessor:
+    def __init__(self, skeletons):
+        self.skeletons = skeletons
+        #self.mean_pose = mean_pose
+        self.filtering_message = "PASS"
+
+    def get(self):
+        assert (self.skeletons is not None)
+
+        # filtering
+        if self.skeletons != []:
+            if self.check_pose_diff():
+                self.skeletons = []
+                self.filtering_message = "pose"
+            # elif self.check_spine_angle():
+            #     self.skeletons = []
+            #     self.filtering_message = "spine angle"
+            # elif self.check_static_motion():
+            #     self.skeletons = []
+            #     self.filtering_message = "motion"
+
+        # if self.skeletons != []:
+        #     self.skeletons = self.skeletons.tolist()
+        #     for i, frame in enumerate(self.skeletons):
+        #         assert not np.isnan(self.skeletons[i]).any()  # missing joints
+
+        return self.skeletons, self.filtering_message
+
+    def check_static_motion(self, verbose=True):
+        def get_variance(skeleton, joint_idx):
+            wrist_pos = skeleton[:, joint_idx]
+            variance = np.sum(np.var(wrist_pos, axis=0))
+            return variance
+
+        left_arm_var = get_variance(self.skeletons, 6)
+        right_arm_var = get_variance(self.skeletons, 9)
+
+        th = 0.0014  # exclude 13110
+        # th = 0.002  # exclude 16905
+        if left_arm_var < th and right_arm_var < th:
+            if verbose:
+                print("skip - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var))
+            return True
+        else:
+            if verbose:
+                print("pass - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var))
+            return False
+
+
+    def check_pose_diff(self, verbose=False):
+#         diff = np.abs(self.skeletons - self.mean_pose) # 186*1
+#         diff = np.mean(diff)
+
+#         # th = 0.017
+#         th = 0.02 #0.02  # exclude 3594
+#         if diff < th:
+#             if verbose:
+#                 print("skip - check_pose_diff {:.5f}".format(diff))
+#             return True
+# #         th = 3.5 #0.02  # exclude 3594
+# #         if 3.5 < diff < 5:
+# #             if verbose:
+# #                 print("skip - check_pose_diff {:.5f}".format(diff))
+# #             return True
+#         else:
+#             if verbose:
+#                 print("pass - check_pose_diff {:.5f}".format(diff))
+        return False
+
+
+    def check_spine_angle(self, verbose=True):
+        def angle_between(v1, v2):
+            v1_u = v1 / np.linalg.norm(v1)
+            v2_u = v2 / np.linalg.norm(v2)
+            return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0))
+
+        angles = []
+        for i in range(self.skeletons.shape[0]):
+            spine_vec = self.skeletons[i, 1] - self.skeletons[i, 0]
+            angle = angle_between(spine_vec, [0, -1, 0])
+            angles.append(angle)
+
+        if np.rad2deg(max(angles)) > 30 or np.rad2deg(np.mean(angles)) > 20:  # exclude 4495
+        # if np.rad2deg(max(angles)) > 20:  # exclude 8270
+            if verbose:
+                print("skip - check_spine_angle {:.5f}, {:.5f}".format(max(angles), np.mean(angles)))
+            return True
+        else:
+            if verbose:
+                print("pass - check_spine_angle {:.5f}".format(max(angles)))
+            return False

dataloaders/beat_sep_lower_single.py

@@ -0,0 +1,730 @@
+import os
+import pickle
+import math
+import shutil
+import numpy as np
+import lmdb as lmdb
+import textgrid as tg
+import pandas as pd
+import torch
+import glob
+import json
+from termcolor import colored
+from loguru import logger
+from collections import defaultdict
+from torch.utils.data import Dataset
+import torch.distributed as dist
+#import pyarrow
+import pickle
+import librosa
+import smplx
+
+from .build_vocab import Vocab
+from .utils.audio_features import Wav2Vec2Model
+from .data_tools import joints_list
+from .utils import rotation_conversions as rc
+from .utils import other_tools
+
+class CustomDataset(Dataset):
+    def __init__(self, args, loader_type, augmentation=None, kwargs=None, build_cache=True):
+        
+        self.audio_file_path = args.audio_file_path
+        self.textgrid_file_path = args.textgrid_file_path
+        self.default_pose_file = "./demo/examples/2_scott_0_1_1.npz"
+        
+        self.args = args
+        self.loader_type = loader_type
+
+        self.rank = 0
+        self.ori_stride = self.args.stride
+        self.ori_length = self.args.pose_length
+        self.alignment = [0,0] # for trinity
+        
+        self.ori_joint_list = joints_list[self.args.ori_joints]
+        self.tar_joint_list = joints_list[self.args.tar_joints]
+        if 'smplx' in self.args.pose_rep:
+            self.joint_mask = np.zeros(len(list(self.ori_joint_list.keys()))*3)
+            self.joints = len(list(self.tar_joint_list.keys()))  
+            for joint_name in self.tar_joint_list:
+                self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        else:
+            self.joints = len(list(self.ori_joint_list.keys()))+1
+            self.joint_mask = np.zeros(self.joints*3)
+            for joint_name in self.tar_joint_list:
+                if joint_name == "Hips":
+                    self.joint_mask[3:6] = 1
+                else:
+                    self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        # select trainable joints
+        self.smplx = smplx.create(
+            self.args.data_path_1+"smplx_models/", 
+            model_type='smplx',
+            gender='NEUTRAL_2020', 
+            use_face_contour=False,
+            num_betas=300,
+            num_expression_coeffs=100, 
+            ext='npz',
+            use_pca=False,
+        ).cuda().eval()
+
+        split_rule = pd.read_csv(args.data_path+"train_test_split.csv")
+        self.selected_file = split_rule.loc[(split_rule['type'] == loader_type) & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+        if args.additional_data and loader_type == 'train':
+            split_b = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            #self.selected_file = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            self.selected_file = pd.concat([self.selected_file, split_b])
+        if self.selected_file.empty:
+            logger.warning(f"{loader_type} is empty for speaker {self.args.training_speakers}, use train set 0-8 instead")
+            self.selected_file = split_rule.loc[(split_rule['type'] == 'train') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            self.selected_file = self.selected_file.iloc[0:8]
+        self.data_dir = args.data_path 
+        
+        if loader_type == "test": 
+            self.args.multi_length_training = [1.0]
+        self.max_length = int(args.pose_length * self.args.multi_length_training[-1])
+        self.max_audio_pre_len = math.floor(args.pose_length / args.pose_fps * self.args.audio_sr)
+        if self.max_audio_pre_len > self.args.test_length*self.args.audio_sr: 
+            self.max_audio_pre_len = self.args.test_length*self.args.audio_sr
+        
+        if args.word_rep is not None:
+            with open(f"{args.data_path}weights/vocab.pkl", 'rb') as f:
+                self.lang_model = pickle.load(f)
+                
+        preloaded_dir = self.args.tmp_dir+'/' + loader_type + f"/{args.pose_rep}_cache"      
+
+        if self.args.beat_align:
+            if not os.path.exists(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy"):
+                self.calculate_mean_velocity(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy")
+            self.avg_vel = np.load(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy")
+            
+        if build_cache and self.rank == 0:
+            self.build_cache(preloaded_dir)
+        self.lmdb_env = lmdb.open(preloaded_dir, readonly=True, lock=False)
+        with self.lmdb_env.begin() as txn:
+            self.n_samples = txn.stat()["entries"]
+        
+        
+
+    
+    def calculate_mean_velocity(self, save_path):
+        self.smplx = smplx.create(
+            self.args.data_path_1+"smplx_models/", 
+            model_type='smplx',
+            gender='NEUTRAL_2020', 
+            use_face_contour=False,
+            num_betas=300,
+            num_expression_coeffs=100, 
+            ext='npz',
+            use_pca=False,
+        ).cuda().eval()
+        dir_p = self.data_dir + self.args.pose_rep + "/"
+        all_list = []
+        from tqdm import tqdm
+        for tar in tqdm(os.listdir(dir_p)):
+            if tar.endswith(".npz"):
+                m_data = np.load(dir_p+tar, allow_pickle=True)
+                betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"]
+                n, c = poses.shape[0], poses.shape[1]
+                betas = betas.reshape(1, 300)
+                betas = np.tile(betas, (n, 1))
+                betas = torch.from_numpy(betas).cuda().float()
+                poses = torch.from_numpy(poses.reshape(n, c)).cuda().float()
+                exps = torch.from_numpy(exps.reshape(n, 100)).cuda().float()
+                trans = torch.from_numpy(trans.reshape(n, 3)).cuda().float()
+                max_length = 128
+                s, r = n//max_length, n%max_length
+                #print(n, s, r)
+                all_tensor = []
+                for i in range(s):
+                    with torch.no_grad():
+                        joints = self.smplx(
+                            betas=betas[i*max_length:(i+1)*max_length], 
+                            transl=trans[i*max_length:(i+1)*max_length], 
+                            expression=exps[i*max_length:(i+1)*max_length], 
+                            jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], 
+                            global_orient=poses[i*max_length:(i+1)*max_length,:3], 
+                            body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], 
+                            left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], 
+                            right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], 
+                            return_verts=True,
+                            return_joints=True,
+                            leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], 
+                            reye_pose=poses[i*max_length:(i+1)*max_length, 72:75],
+                        )['joints'][:, :55, :].reshape(max_length, 55*3)
+                    all_tensor.append(joints)
+                if r != 0:
+                    with torch.no_grad():
+                        joints = self.smplx(
+                            betas=betas[s*max_length:s*max_length+r], 
+                            transl=trans[s*max_length:s*max_length+r], 
+                            expression=exps[s*max_length:s*max_length+r], 
+                            jaw_pose=poses[s*max_length:s*max_length+r, 66:69], 
+                            global_orient=poses[s*max_length:s*max_length+r,:3], 
+                            body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], 
+                            left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], 
+                            right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], 
+                            return_verts=True,
+                            return_joints=True,
+                            leye_pose=poses[s*max_length:s*max_length+r, 69:72], 
+                            reye_pose=poses[s*max_length:s*max_length+r, 72:75],
+                        )['joints'][:, :55, :].reshape(r, 55*3)
+                    all_tensor.append(joints)
+                joints = torch.cat(all_tensor, axis=0)
+                joints = joints.permute(1, 0)
+                dt = 1/30
+            # first steps is forward diff (t+1 - t) / dt
+                init_vel = (joints[:, 1:2] - joints[:, :1]) / dt
+                # middle steps are second order (t+1 - t-1) / 2dt
+                middle_vel = (joints[:, 2:] - joints[:, 0:-2]) / (2 * dt)
+                # last step is backward diff (t - t-1) / dt
+                final_vel = (joints[:, -1:] - joints[:, -2:-1]) / dt
+                #print(joints.shape, init_vel.shape, middle_vel.shape, final_vel.shape)
+                vel_seq = torch.cat([init_vel, middle_vel, final_vel], dim=1).permute(1, 0).reshape(n, 55, 3)
+                #print(vel_seq.shape)
+                #.permute(1, 0).reshape(n, 55, 3)
+                vel_seq_np = vel_seq.cpu().numpy()
+                vel_joints_np = np.linalg.norm(vel_seq_np, axis=2) # n * 55
+                all_list.append(vel_joints_np)
+        avg_vel = np.mean(np.concatenate(all_list, axis=0),axis=0) # 55
+        np.save(save_path, avg_vel)
+        
+    
+    def build_cache(self, preloaded_dir):
+        logger.info(f"Audio bit rate: {self.args.audio_fps}")
+        logger.info("Reading data '{}'...".format(self.data_dir))
+        logger.info("Creating the dataset cache...")
+        if self.args.new_cache:
+            if os.path.exists(preloaded_dir):
+                shutil.rmtree(preloaded_dir)
+        if os.path.exists(preloaded_dir):
+            logger.info("Found the cache {}".format(preloaded_dir))
+        elif self.loader_type == "test":
+            self.cache_generation(
+                preloaded_dir, True, 
+                0, 0,
+                is_test=True)
+        else:
+            self.cache_generation(
+                preloaded_dir, self.args.disable_filtering, 
+                self.args.clean_first_seconds, self.args.clean_final_seconds,
+                is_test=False)
+        
+    def __len__(self):
+        return self.n_samples
+    
+
+    def cache_generation(self, out_lmdb_dir, disable_filtering, clean_first_seconds,  clean_final_seconds, is_test=False):
+        # if "wav2vec2" in self.args.audio_rep:
+        #     self.wav2vec_model = Wav2Vec2Model.from_pretrained(f"{self.args.data_path_1}/hub/transformer/wav2vec2-base-960h")
+        #     self.wav2vec_model.feature_extractor._freeze_parameters()
+        #     self.wav2vec_model = self.wav2vec_model.cuda()
+        #     self.wav2vec_model.eval()
+        
+        self.n_out_samples = 0
+        # create db for samples
+        if not os.path.exists(out_lmdb_dir): os.makedirs(out_lmdb_dir)
+        dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 500))# 500G
+        n_filtered_out = defaultdict(int)
+    
+
+        #f_name = file_name["id"]
+        ext = ".npz" if "smplx" in self.args.pose_rep else ".bvh"
+        pose_file = self.default_pose_file
+        pose_each_file = []
+        trans_each_file = []
+        trans_v_each_file = []
+        shape_each_file = []
+        audio_each_file = []
+        facial_each_file = []
+        word_each_file = []
+        emo_each_file = []
+        sem_each_file = []
+        vid_each_file = []
+        id_pose = "tmp" #1_wayne_0_1_1
+        
+        logger.info(colored(f"# ---- Building cache for Pose   {id_pose} ---- #", "blue"))
+        if "smplx" in self.args.pose_rep:
+            pose_data = np.load(pose_file, allow_pickle=True)
+            assert 30%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 30'
+            stride = int(30/self.args.pose_fps)
+            pose_each_file = pose_data["poses"][::stride] 
+            trans_each_file = pose_data["trans"][::stride]
+            trans_each_file[:,0] = trans_each_file[:,0] - trans_each_file[0,0]
+            trans_each_file[:,2] = trans_each_file[:,2] - trans_each_file[0,2]
+            trans_v_each_file = np.zeros_like(trans_each_file)
+            trans_v_each_file[1:,0] = trans_each_file[1:,0] - trans_each_file[:-1,0]
+            trans_v_each_file[0,0] = trans_v_each_file[1,0]
+            trans_v_each_file[1:,2] = trans_each_file[1:,2] - trans_each_file[:-1,2]
+            trans_v_each_file[0,2] = trans_v_each_file[1,2]
+            trans_v_each_file[:,1] = trans_each_file[:,1]
+            shape_each_file = np.repeat(pose_data["betas"].reshape(1, 300), pose_each_file.shape[0], axis=0)
+            
+            assert self.args.pose_fps == 30, "should 30"
+            m_data = np.load(pose_file, allow_pickle=True)
+            betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"]
+            n, c = poses.shape[0], poses.shape[1]
+            betas = betas.reshape(1, 300)
+            betas = np.tile(betas, (n, 1))
+            betas = torch.from_numpy(betas).cuda().float()
+            poses = torch.from_numpy(poses.reshape(n, c)).cuda().float()
+            exps = torch.from_numpy(exps.reshape(n, 100)).cuda().float()
+            trans = torch.from_numpy(trans.reshape(n, 3)).cuda().float()
+            max_length = 128    # 为什么这里需要一个max_length
+            s, r = n//max_length, n%max_length
+            #print(n, s, r)
+            all_tensor = []
+            for i in range(s):
+                with torch.no_grad():
+                    joints = self.smplx(
+                        betas=betas[i*max_length:(i+1)*max_length], 
+                        transl=trans[i*max_length:(i+1)*max_length], 
+                        expression=exps[i*max_length:(i+1)*max_length], 
+                        jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], 
+                        global_orient=poses[i*max_length:(i+1)*max_length,:3], 
+                        body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], 
+                        left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], 
+                        right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], 
+                        return_verts=True,
+                        return_joints=True,
+                        leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], 
+                        reye_pose=poses[i*max_length:(i+1)*max_length, 72:75],
+                    )['joints'][:, (7,8,10,11), :].reshape(max_length, 4, 3).cpu()
+                all_tensor.append(joints)
+            if r != 0:
+                with torch.no_grad():
+                    joints = self.smplx(
+                        betas=betas[s*max_length:s*max_length+r], 
+                        transl=trans[s*max_length:s*max_length+r], 
+                        expression=exps[s*max_length:s*max_length+r], 
+                        jaw_pose=poses[s*max_length:s*max_length+r, 66:69], 
+                        global_orient=poses[s*max_length:s*max_length+r,:3], 
+                        body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], 
+                        left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], 
+                        right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], 
+                        return_verts=True,
+                        return_joints=True,
+                        leye_pose=poses[s*max_length:s*max_length+r, 69:72], 
+                        reye_pose=poses[s*max_length:s*max_length+r, 72:75],
+                    )['joints'][:, (7,8,10,11), :].reshape(r, 4, 3).cpu()
+                all_tensor.append(joints)
+            joints = torch.cat(all_tensor, axis=0) # all, 4, 3
+            # print(joints.shape)
+            feetv = torch.zeros(joints.shape[1], joints.shape[0])
+            joints = joints.permute(1, 0, 2)
+            #print(joints.shape, feetv.shape)
+            feetv[:, :-1] = (joints[:, 1:] - joints[:, :-1]).norm(dim=-1)
+            #print(feetv.shape)
+            contacts = (feetv < 0.01).numpy().astype(float)
+            # print(contacts.shape, contacts)
+            contacts = contacts.transpose(1, 0)
+            pose_each_file = pose_each_file * self.joint_mask
+            pose_each_file = pose_each_file[:, self.joint_mask.astype(bool)]
+            pose_each_file = np.concatenate([pose_each_file, contacts], axis=1)
+            # print(pose_each_file.shape)
+            
+            
+            if self.args.facial_rep is not None:
+                logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #")
+                facial_each_file = pose_data["expressions"][::stride]
+                if self.args.facial_norm: 
+                    facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial
+                    
+        if self.args.id_rep is not None:
+            vid_each_file = np.repeat(np.array(int(999)-1).reshape(1, 1), pose_each_file.shape[0], axis=0)
+    
+        if self.args.audio_rep is not None:
+            logger.info(f"# ---- Building cache for Audio  {id_pose} and Pose {id_pose} ---- #")
+            audio_file = self.audio_file_path
+            if not os.path.exists(audio_file):
+                logger.warning(f"# ---- file not found for Audio  {id_pose}, skip all files with the same id ---- #")
+                self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index)
+
+            audio_save_path = audio_file.replace("wave16k", "onset_amplitude").replace(".wav", ".npy")
+
+            if self.args.audio_rep == "onset+amplitude":
+                audio_each_file, sr = librosa.load(audio_file)
+                audio_each_file = librosa.resample(audio_each_file, orig_sr=sr, target_sr=self.args.audio_sr)
+                from numpy.lib import stride_tricks
+                frame_length = 1024
+                # hop_length = 512
+                shape = (audio_each_file.shape[-1] - frame_length + 1, frame_length)
+                strides = (audio_each_file.strides[-1], audio_each_file.strides[-1])
+                rolling_view = stride_tricks.as_strided(audio_each_file, shape=shape, strides=strides)
+                amplitude_envelope = np.max(np.abs(rolling_view), axis=1)
+                # pad the last frame_length-1 samples
+                amplitude_envelope = np.pad(amplitude_envelope, (0, frame_length-1), mode='constant', constant_values=amplitude_envelope[-1])
+                audio_onset_f = librosa.onset.onset_detect(y=audio_each_file, sr=self.args.audio_sr, units='frames')
+                onset_array = np.zeros(len(audio_each_file), dtype=float)
+                onset_array[audio_onset_f] = 1.0
+                # print(amplitude_envelope.shape, audio_each_file.shape, onset_array.shape)
+                audio_each_file = np.concatenate([amplitude_envelope.reshape(-1, 1), onset_array.reshape(-1, 1)], axis=1)
+
+                
+            elif self.args.audio_rep == "mfcc":
+                audio_each_file = librosa.feature.melspectrogram(y=audio_each_file, sr=self.args.audio_sr, n_mels=128, hop_length=int(self.args.audio_sr/self.args.audio_fps))
+                audio_each_file = audio_each_file.transpose(1, 0)
+                # print(audio_each_file.shape, pose_each_file.shape)
+            if self.args.audio_norm and self.args.audio_rep == "wave16k": 
+                audio_each_file = (audio_each_file - self.mean_audio) / self.std_audio
+                
+        time_offset = 0
+        if self.args.word_rep is not None:
+            logger.info(f"# ---- Building cache for Word   {id_pose} and Pose {id_pose} ---- #")
+            word_file = self.textgrid_file_path
+            if not os.path.exists(word_file):
+                logger.warning(f"# ---- file not found for Word   {id_pose}, skip all files with the same id ---- #")
+                self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index)
+            word_save_path = f"{self.data_dir}{self.args.t_pre_encoder}/{id_pose}.npy"
+
+            tgrid = tg.TextGrid.fromFile(word_file)
+
+            for i in range(pose_each_file.shape[0]):
+                found_flag = False
+                current_time = i/self.args.pose_fps + time_offset
+                j_last = 0
+                for j, word in enumerate(tgrid[0]): 
+                    word_n, word_s, word_e = word.mark, word.minTime, word.maxTime
+                    if word_s<=current_time and current_time<=word_e:
+                        if word_n == " ":
+                            word_each_file.append(self.lang_model.PAD_token)
+                        else:
+                            word_each_file.append(self.lang_model.get_word_index(word_n))
+                        found_flag = True
+                        j_last = j
+                        break
+                    else: continue   
+                if not found_flag: 
+                    word_each_file.append(self.lang_model.UNK_token)
+            word_each_file = np.array(word_each_file)
+
+
+            
+        if self.args.emo_rep is not None:
+            logger.info(f"# ---- Building cache for Emo    {id_pose} and Pose {id_pose} ---- #")
+            rtype, start = int(id_pose.split('_')[3]), int(id_pose.split('_')[3])
+            if rtype == 0 or rtype == 2 or rtype == 4 or rtype == 6:
+                if start >= 1 and start <= 64:
+                    score = 0
+                elif start >= 65 and start <= 72:
+                    score = 1
+                elif start >= 73 and start <= 80:
+                    score = 2
+                elif start >= 81 and start <= 86:
+                    score = 3
+                elif start >= 87 and start <= 94:
+                    score = 4
+                elif start >= 95 and start <= 102:
+                    score = 5
+                elif start >= 103 and start <= 110:
+                    score = 6
+                elif start >= 111 and start <= 118:
+                    score = 7
+                else: pass
+            else:
+                # you may denote as unknown in the future
+                score = 0
+            emo_each_file = np.repeat(np.array(score).reshape(1, 1), pose_each_file.shape[0], axis=0)    
+            #print(emo_each_file)
+            
+        if self.args.sem_rep is not None:
+            logger.info(f"# ---- Building cache for Sem    {id_pose} and Pose {id_pose} ---- #")
+            sem_file = f"{self.data_dir}{self.args.sem_rep}/{id_pose}.txt" 
+            sem_all = pd.read_csv(sem_file, 
+                sep='\t', 
+                names=["name", "start_time", "end_time", "duration", "score", "keywords"])
+            # we adopt motion-level semantic score here. 
+            for i in range(pose_each_file.shape[0]):
+                found_flag = False
+                for j, (start, end, score) in enumerate(zip(sem_all['start_time'],sem_all['end_time'], sem_all['score'])):
+                    current_time = i/self.args.pose_fps + time_offset
+                    if start<=current_time and current_time<=end: 
+                        sem_each_file.append(score)
+                        found_flag=True
+                        break
+                    else: continue 
+                if not found_flag: sem_each_file.append(0.)
+            sem_each_file = np.array(sem_each_file)
+            #print(sem_each_file)
+        
+        filtered_result = self._sample_from_clip(
+            dst_lmdb_env,
+            audio_each_file, pose_each_file, trans_each_file, trans_v_each_file,shape_each_file, facial_each_file, word_each_file,
+            vid_each_file, emo_each_file, sem_each_file,
+            disable_filtering, clean_first_seconds, clean_final_seconds, is_test,
+            ) 
+        for type in filtered_result.keys():
+            n_filtered_out[type] += filtered_result[type]
+                            
+        
+        
+        
+#### ---------for_end------------ ####
+        with dst_lmdb_env.begin() as txn:
+            logger.info(colored(f"no. of samples: {txn.stat()['entries']}", "cyan"))
+            n_total_filtered = 0
+            for type, n_filtered in n_filtered_out.items():
+                logger.info("{}: {}".format(type, n_filtered))
+                n_total_filtered += n_filtered
+            logger.info(colored("no. of excluded samples: {} ({:.1f}%)".format(
+                n_total_filtered, 100 * n_total_filtered / (txn.stat()["entries"] + n_total_filtered)), "cyan"))
+        dst_lmdb_env.sync()
+        dst_lmdb_env.close()
+    
+    def _sample_from_clip(
+        self, dst_lmdb_env, audio_each_file, pose_each_file, trans_each_file, trans_v_each_file,shape_each_file, facial_each_file, word_each_file,
+        vid_each_file, emo_each_file, sem_each_file,
+        disable_filtering, clean_first_seconds, clean_final_seconds, is_test,
+        ):
+        """
+        for data cleaning, we ignore the data for first and final n s
+        for test, we return all data 
+        """
+        # audio_start = int(self.alignment[0] * self.args.audio_fps)
+        # pose_start = int(self.alignment[1] * self.args.pose_fps)
+        #logger.info(f"before: {audio_each_file.shape} {pose_each_file.shape}")
+        # audio_each_file = audio_each_file[audio_start:]
+        # pose_each_file = pose_each_file[pose_start:]
+        # trans_each_file = 
+        #logger.info(f"after alignment: {audio_each_file.shape} {pose_each_file.shape}")
+        #print(pose_each_file.shape)
+        round_seconds_skeleton = pose_each_file.shape[0] // self.args.pose_fps  # assume 1500 frames / 15 fps = 100 s
+        #print(round_seconds_skeleton)
+        if audio_each_file is not None:
+            if self.args.audio_rep != "wave16k":
+                round_seconds_audio = len(audio_each_file) // self.args.audio_fps # assume 16,000,00 / 16,000 = 100 s
+            elif self.args.audio_rep == "mfcc":
+                round_seconds_audio = audio_each_file.shape[0] // self.args.audio_fps
+            else:
+                round_seconds_audio = audio_each_file.shape[0] // self.args.audio_sr
+            if facial_each_file is not None:
+                round_seconds_facial = facial_each_file.shape[0] // self.args.pose_fps
+                logger.info(f"audio: {round_seconds_audio}s, pose: {round_seconds_skeleton}s, facial: {round_seconds_facial}s")
+                round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton, round_seconds_facial)
+                max_round = max(round_seconds_audio, round_seconds_skeleton, round_seconds_facial)
+                if round_seconds_skeleton != max_round: 
+                    logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s")  
+            else:
+                logger.info(f"pose: {round_seconds_skeleton}s, audio: {round_seconds_audio}s")
+                round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton)
+                max_round = max(round_seconds_audio, round_seconds_skeleton)
+                if round_seconds_skeleton != max_round: 
+                    logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s")
+        
+        clip_s_t, clip_e_t = clean_first_seconds, round_seconds_skeleton - clean_final_seconds # assume [10, 90]s
+        clip_s_f_audio, clip_e_f_audio = self.args.audio_fps * clip_s_t, clip_e_t * self.args.audio_fps # [160,000,90*160,000]
+        clip_s_f_pose, clip_e_f_pose = clip_s_t * self.args.pose_fps, clip_e_t * self.args.pose_fps # [150,90*15]
+        
+        
+        for ratio in self.args.multi_length_training:
+            if is_test:# stride = length for test
+                cut_length = clip_e_f_pose - clip_s_f_pose
+                self.args.stride = cut_length
+                self.max_length = cut_length
+            else:
+                self.args.stride = int(ratio*self.ori_stride)
+                cut_length = int(self.ori_length*ratio)
+                
+            num_subdivision = math.floor((clip_e_f_pose - clip_s_f_pose - cut_length) / self.args.stride) + 1
+            logger.info(f"pose from frame {clip_s_f_pose} to {clip_e_f_pose}, length {cut_length}")
+            logger.info(f"{num_subdivision} clips is expected with stride {self.args.stride}")
+            
+            if audio_each_file is not None:
+                audio_short_length = math.floor(cut_length / self.args.pose_fps * self.args.audio_fps)
+                """
+                for audio sr = 16000, fps = 15, pose_length = 34, 
+                audio short length = 36266.7 -> 36266
+                this error is fine.
+                """
+                logger.info(f"audio from frame {clip_s_f_audio} to {clip_e_f_audio}, length {audio_short_length}")
+             
+            n_filtered_out = defaultdict(int)
+            sample_pose_list = []
+            sample_audio_list = []
+            sample_facial_list = []
+            sample_shape_list = []
+            sample_word_list = []
+            sample_emo_list = []
+            sample_sem_list = []
+            sample_vid_list = []
+            sample_trans_list = []
+            sample_trans_v_list = []
+           
+            for i in range(num_subdivision): # cut into around 2s chip, (self npose)
+                start_idx = clip_s_f_pose + i * self.args.stride
+                fin_idx = start_idx + cut_length 
+                sample_pose = pose_each_file[start_idx:fin_idx]
+
+                sample_trans = trans_each_file[start_idx:fin_idx]
+                sample_trans_v = trans_v_each_file[start_idx:fin_idx]
+                sample_shape = shape_each_file[start_idx:fin_idx]
+                # print(sample_pose.shape)
+                if self.args.audio_rep is not None:
+                    audio_start = clip_s_f_audio + math.floor(i * self.args.stride * self.args.audio_fps / self.args.pose_fps)
+                    audio_end = audio_start + audio_short_length
+                    sample_audio = audio_each_file[audio_start:audio_end]
+                else:
+                    sample_audio = np.array([-1])
+                sample_facial = facial_each_file[start_idx:fin_idx] if self.args.facial_rep is not None else np.array([-1])
+                sample_word = word_each_file[start_idx:fin_idx] if self.args.word_rep is not None else np.array([-1])
+                sample_emo = emo_each_file[start_idx:fin_idx] if self.args.emo_rep is not None else np.array([-1])
+                sample_sem = sem_each_file[start_idx:fin_idx] if self.args.sem_rep is not None else np.array([-1])
+                sample_vid = vid_each_file[start_idx:fin_idx] if self.args.id_rep is not None else np.array([-1])
+                
+                if sample_pose.any() != None:
+                    # filtering motion skeleton data
+                    sample_pose, filtering_message = MotionPreprocessor(sample_pose).get()
+                    is_correct_motion = (sample_pose is not None)
+                    if is_correct_motion or disable_filtering:
+                        sample_pose_list.append(sample_pose)
+                        sample_audio_list.append(sample_audio)
+                        sample_facial_list.append(sample_facial)
+                        sample_shape_list.append(sample_shape)
+                        sample_word_list.append(sample_word)
+                        sample_vid_list.append(sample_vid)
+                        sample_emo_list.append(sample_emo)
+                        sample_sem_list.append(sample_sem)
+                        sample_trans_list.append(sample_trans)
+                        sample_trans_v_list.append(sample_trans_v)
+                    else:
+                        n_filtered_out[filtering_message] += 1
+
+            if len(sample_pose_list) > 0:
+                with dst_lmdb_env.begin(write=True) as txn:
+                    for pose, audio, facial, shape, word, vid, emo, sem, trans,trans_v in zip(
+                        sample_pose_list,
+                        sample_audio_list,
+                        sample_facial_list,
+                        sample_shape_list,
+                        sample_word_list,
+                        sample_vid_list,
+                        sample_emo_list,
+                        sample_sem_list,
+                        sample_trans_list,
+                        sample_trans_v_list,):
+                        k = "{:005}".format(self.n_out_samples).encode("ascii")
+                        v = [pose, audio, facial, shape, word, emo, sem, vid, trans,trans_v]
+                        v = pickle.dumps(v,5)
+                        txn.put(k, v)
+                        self.n_out_samples += 1
+        return n_filtered_out
+
+    def __getitem__(self, idx):
+        with self.lmdb_env.begin(write=False) as txn:
+            key = "{:005}".format(idx).encode("ascii")
+            sample = txn.get(key)
+            sample = pickle.loads(sample)
+            tar_pose, in_audio, in_facial, in_shape, in_word, emo, sem, vid, trans,trans_v = sample
+            #print(in_shape)
+            #vid = torch.from_numpy(vid).int()
+            emo = torch.from_numpy(emo).int()
+            sem = torch.from_numpy(sem).float() 
+            in_audio = torch.from_numpy(in_audio).float() 
+            in_word = torch.from_numpy(in_word).float() if self.args.word_cache else torch.from_numpy(in_word).int() 
+            if self.loader_type == "test":
+                tar_pose = torch.from_numpy(tar_pose).float()
+                trans = torch.from_numpy(trans).float()
+                trans_v = torch.from_numpy(trans_v).float()
+                in_facial = torch.from_numpy(in_facial).float()
+                vid = torch.from_numpy(vid).float()
+                in_shape = torch.from_numpy(in_shape).float()
+            else:
+                in_shape = torch.from_numpy(in_shape).reshape((in_shape.shape[0], -1)).float()
+                trans = torch.from_numpy(trans).reshape((trans.shape[0], -1)).float()
+                trans_v = torch.from_numpy(trans_v).reshape((trans_v.shape[0], -1)).float()
+                vid = torch.from_numpy(vid).reshape((vid.shape[0], -1)).float()
+                tar_pose = torch.from_numpy(tar_pose).reshape((tar_pose.shape[0], -1)).float()
+                in_facial = torch.from_numpy(in_facial).reshape((in_facial.shape[0], -1)).float()
+            return {"pose":tar_pose, "audio":in_audio, "facial":in_facial, "beta": in_shape, "word":in_word, "id":vid, "emo":emo, "sem":sem, "trans":trans,"trans_v":trans_v}
+
+         
+class MotionPreprocessor:
+    def __init__(self, skeletons):
+        self.skeletons = skeletons
+        #self.mean_pose = mean_pose
+        self.filtering_message = "PASS"
+
+    def get(self):
+        assert (self.skeletons is not None)
+
+        # filtering
+        if self.skeletons is not None:
+            if self.check_pose_diff():
+                self.skeletons = []
+                self.filtering_message = "pose"
+            # elif self.check_spine_angle():
+            #     self.skeletons = []
+            #     self.filtering_message = "spine angle"
+            # elif self.check_static_motion():
+            #     self.skeletons = []
+            #     self.filtering_message = "motion"
+
+        # if self.skeletons is not None:
+        #     self.skeletons = self.skeletons.tolist()
+        #     for i, frame in enumerate(self.skeletons):
+        #         assert not np.isnan(self.skeletons[i]).any()  # missing joints
+
+        return self.skeletons, self.filtering_message
+
+    def check_static_motion(self, verbose=True):
+        def get_variance(skeleton, joint_idx):
+            wrist_pos = skeleton[:, joint_idx]
+            variance = np.sum(np.var(wrist_pos, axis=0))
+            return variance
+
+        left_arm_var = get_variance(self.skeletons, 6)
+        right_arm_var = get_variance(self.skeletons, 9)
+
+        th = 0.0014  # exclude 13110
+        # th = 0.002  # exclude 16905
+        if left_arm_var < th and right_arm_var < th:
+            if verbose:
+                print("skip - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var))
+            return True
+        else:
+            if verbose:
+                print("pass - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var))
+            return False
+
+
+    def check_pose_diff(self, verbose=False):
+#         diff = np.abs(self.skeletons - self.mean_pose) # 186*1
+#         diff = np.mean(diff)
+
+#         # th = 0.017
+#         th = 0.02 #0.02  # exclude 3594
+#         if diff < th:
+#             if verbose:
+#                 print("skip - check_pose_diff {:.5f}".format(diff))
+#             return True
+# #         th = 3.5 #0.02  # exclude 3594
+# #         if 3.5 < diff < 5:
+# #             if verbose:
+# #                 print("skip - check_pose_diff {:.5f}".format(diff))
+# #             return True
+#         else:
+#             if verbose:
+#                 print("pass - check_pose_diff {:.5f}".format(diff))
+        return False
+
+
+    def check_spine_angle(self, verbose=True):
+        def angle_between(v1, v2):
+            v1_u = v1 / np.linalg.norm(v1)
+            v2_u = v2 / np.linalg.norm(v2)
+            return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0))
+
+        angles = []
+        for i in range(self.skeletons.shape[0]):
+            spine_vec = self.skeletons[i, 1] - self.skeletons[i, 0]
+            angle = angle_between(spine_vec, [0, -1, 0])
+            angles.append(angle)
+
+        if np.rad2deg(max(angles)) > 30 or np.rad2deg(np.mean(angles)) > 20:  # exclude 4495
+        # if np.rad2deg(max(angles)) > 20:  # exclude 8270
+            if verbose:
+                print("skip - check_spine_angle {:.5f}, {:.5f}".format(max(angles), np.mean(angles)))
+            return True
+        else:
+            if verbose:
+                print("pass - check_spine_angle {:.5f}".format(max(angles)))
+            return False

dataloaders/beat_smplx2020.py

@@ -0,0 +1,763 @@
+import os
+import pickle
+import math
+import shutil
+import numpy as np
+import lmdb as lmdb
+import textgrid as tg
+import pandas as pd
+import torch
+import glob
+import json
+from termcolor import colored
+from loguru import logger
+from collections import defaultdict
+from torch.utils.data import Dataset
+import torch.distributed as dist
+import pyarrow
+import librosa
+import smplx
+
+from .build_vocab import Vocab
+from .utils.audio_features import Wav2Vec2Model
+from .data_tools import joints_list
+from .utils import rotation_conversions as rc
+from .utils import other_tools
+
+class CustomDataset(Dataset):
+    def __init__(self, args, loader_type, augmentation=None, kwargs=None, build_cache=True):
+        self.args = args
+        self.loader_type = loader_type
+
+        self.rank = dist.get_rank()
+        self.ori_stride = self.args.stride
+        self.ori_length = self.args.pose_length
+        self.alignment = [0,0] # for trinity
+        
+        self.ori_joint_list = joints_list[self.args.ori_joints]
+        self.tar_joint_list = joints_list[self.args.tar_joints]
+        if 'smplx' in self.args.pose_rep:
+            self.joint_mask = np.zeros(len(list(self.ori_joint_list.keys()))*3)
+            self.joints = len(list(self.ori_joint_list.keys()))  
+            for joint_name in self.tar_joint_list:
+                self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        else:
+            self.joints = len(list(self.ori_joint_list.keys()))+1
+            self.joint_mask = np.zeros(self.joints*3)
+            for joint_name in self.tar_joint_list:
+                if joint_name == "Hips":
+                    self.joint_mask[3:6] = 1
+                else:
+                    self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        # select trainable joints
+        
+        split_rule = pd.read_csv(args.data_path+"train_test_split.csv")
+        self.selected_file = split_rule.loc[(split_rule['type'] == loader_type) & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+        if args.additional_data and loader_type == 'train':
+            split_b = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            #self.selected_file = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            self.selected_file = pd.concat([self.selected_file, split_b])
+        if self.selected_file.empty:
+            logger.warning(f"{loader_type} is empty for speaker {self.args.training_speakers}, use train set 0-8 instead")
+            self.selected_file = split_rule.loc[(split_rule['type'] == 'train') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            self.selected_file = self.selected_file.iloc[0:8]
+        self.data_dir = args.data_path 
+        
+        if loader_type == "test": 
+            self.args.multi_length_training = [1.0]
+        self.max_length = int(args.pose_length * self.args.multi_length_training[-1])
+        self.max_audio_pre_len = math.floor(args.pose_length / args.pose_fps * self.args.audio_sr)
+        if self.max_audio_pre_len > self.args.test_length*self.args.audio_sr: 
+            self.max_audio_pre_len = self.args.test_length*self.args.audio_sr
+        
+        if args.word_rep is not None:
+            with open(f"{args.data_path}weights/vocab.pkl", 'rb') as f:
+                self.lang_model = pickle.load(f)
+                
+        preloaded_dir = self.args.root_path + self.args.cache_path + loader_type + f"/{args.pose_rep}_cache"      
+        # if args.pose_norm:
+        #     # careful for rotation vectors
+        #     if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy"):
+        #         self.calculate_mean_pose()
+        #     self.mean_pose = np.load(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy")
+        #     self.std_pose = np.load(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_std.npy")
+        # if args.audio_norm:
+        #     if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/bvh_mean.npy"):
+        #         self.calculate_mean_audio()
+        #     self.mean_audio = np.load(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/npy_mean.npy")
+        #     self.std_audio = np.load(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/npy_std.npy")
+        # if args.facial_norm:
+        #     if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy"):
+        #         self.calculate_mean_face()
+        #     self.mean_facial = np.load(args.data_path+args.mean_pose_path+f"{args.facial_rep}/json_mean.npy")
+        #     self.std_facial = np.load(args.data_path+args.mean_pose_path+f"{args.facial_rep}/json_std.npy")
+        if self.args.beat_align:
+            if not os.path.exists(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy"):
+                self.calculate_mean_velocity(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy")
+            self.avg_vel = np.load(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy")
+            
+        if build_cache and self.rank == 0:
+            self.build_cache(preloaded_dir)
+        self.lmdb_env = lmdb.open(preloaded_dir, readonly=True, lock=False)
+        with self.lmdb_env.begin() as txn:
+            self.n_samples = txn.stat()["entries"] 
+
+    
+    def calculate_mean_velocity(self, save_path):
+        self.smplx = smplx.create(
+            self.args.data_path_1+"smplx_models/", 
+            model_type='smplx',
+            gender='NEUTRAL_2020', 
+            use_face_contour=False,
+            num_betas=300,
+            num_expression_coeffs=100, 
+            ext='npz',
+            use_pca=False,
+        ).cuda().eval()
+        dir_p = self.data_dir + self.args.pose_rep + "/"
+        all_list = []
+        from tqdm import tqdm
+        for tar in tqdm(os.listdir(dir_p)):
+            if tar.endswith(".npz"):
+                m_data = np.load(dir_p+tar, allow_pickle=True)
+                betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"]
+                n, c = poses.shape[0], poses.shape[1]
+                betas = betas.reshape(1, 300)
+                betas = np.tile(betas, (n, 1))
+                betas = torch.from_numpy(betas).cuda().float()
+                poses = torch.from_numpy(poses.reshape(n, c)).cuda().float()
+                exps = torch.from_numpy(exps.reshape(n, 100)).cuda().float()
+                trans = torch.from_numpy(trans.reshape(n, 3)).cuda().float()
+                max_length = 128
+                s, r = n//max_length, n%max_length
+                #print(n, s, r)
+                all_tensor = []
+                for i in range(s):
+                    with torch.no_grad():
+                        joints = self.smplx(
+                            betas=betas[i*max_length:(i+1)*max_length], 
+                            transl=trans[i*max_length:(i+1)*max_length], 
+                            expression=exps[i*max_length:(i+1)*max_length], 
+                            jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], 
+                            global_orient=poses[i*max_length:(i+1)*max_length,:3], 
+                            body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], 
+                            left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], 
+                            right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], 
+                            return_verts=True,
+                            return_joints=True,
+                            leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], 
+                            reye_pose=poses[i*max_length:(i+1)*max_length, 72:75],
+                        )['joints'][:, :55, :].reshape(max_length, 55*3)
+                    all_tensor.append(joints)
+                if r != 0:
+                    with torch.no_grad():
+                        joints = self.smplx(
+                            betas=betas[s*max_length:s*max_length+r], 
+                            transl=trans[s*max_length:s*max_length+r], 
+                            expression=exps[s*max_length:s*max_length+r], 
+                            jaw_pose=poses[s*max_length:s*max_length+r, 66:69], 
+                            global_orient=poses[s*max_length:s*max_length+r,:3], 
+                            body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], 
+                            left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], 
+                            right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], 
+                            return_verts=True,
+                            return_joints=True,
+                            leye_pose=poses[s*max_length:s*max_length+r, 69:72], 
+                            reye_pose=poses[s*max_length:s*max_length+r, 72:75],
+                        )['joints'][:, :55, :].reshape(r, 55*3)
+                    all_tensor.append(joints)
+                joints = torch.cat(all_tensor, axis=0)
+                joints = joints.permute(1, 0)
+                dt = 1/30
+            # first steps is forward diff (t+1 - t) / dt
+                init_vel = (joints[:, 1:2] - joints[:, :1]) / dt
+                # middle steps are second order (t+1 - t-1) / 2dt
+                middle_vel = (joints[:, 2:] - joints[:, 0:-2]) / (2 * dt)
+                # last step is backward diff (t - t-1) / dt
+                final_vel = (joints[:, -1:] - joints[:, -2:-1]) / dt
+                #print(joints.shape, init_vel.shape, middle_vel.shape, final_vel.shape)
+                vel_seq = torch.cat([init_vel, middle_vel, final_vel], dim=1).permute(1, 0).reshape(n, 55, 3)
+                #print(vel_seq.shape)
+                #.permute(1, 0).reshape(n, 55, 3)
+                vel_seq_np = vel_seq.cpu().numpy()
+                vel_joints_np = np.linalg.norm(vel_seq_np, axis=2) # n * 55
+                all_list.append(vel_joints_np)
+        avg_vel = np.mean(np.concatenate(all_list, axis=0),axis=0) # 55
+        np.save(save_path, avg_vel)
+        
+    
+    def build_cache(self, preloaded_dir):
+        logger.info(f"Audio bit rate: {self.args.audio_fps}")
+        logger.info("Reading data '{}'...".format(self.data_dir))
+        logger.info("Creating the dataset cache...")
+        if self.args.new_cache:
+            if os.path.exists(preloaded_dir):
+                shutil.rmtree(preloaded_dir)
+        if os.path.exists(preloaded_dir):
+            logger.info("Found the cache {}".format(preloaded_dir))
+        elif self.loader_type == "test":
+            self.cache_generation(
+                preloaded_dir, True, 
+                0, 0,
+                is_test=True)
+        else:
+            self.cache_generation(
+                preloaded_dir, self.args.disable_filtering, 
+                self.args.clean_first_seconds, self.args.clean_final_seconds,
+                is_test=False)
+        
+    def __len__(self):
+        return self.n_samples
+    
+
+    def cache_generation(self, out_lmdb_dir, disable_filtering, clean_first_seconds,  clean_final_seconds, is_test=False):
+        # if "wav2vec2" in self.args.audio_rep:
+        #     self.wav2vec_model = Wav2Vec2Model.from_pretrained(f"{self.args.data_path_1}/hub/transformer/wav2vec2-base-960h")
+        #     self.wav2vec_model.feature_extractor._freeze_parameters()
+        #     self.wav2vec_model = self.wav2vec_model.cuda()
+        #     self.wav2vec_model.eval()
+        
+        self.n_out_samples = 0
+        # create db for samples
+        if not os.path.exists(out_lmdb_dir): os.makedirs(out_lmdb_dir)
+        dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 50))# 50G
+        n_filtered_out = defaultdict(int)
+    
+        for index, file_name in self.selected_file.iterrows():
+            f_name = file_name["id"]
+            ext = ".npz" if "smplx" in self.args.pose_rep else ".bvh"
+            pose_file = self.data_dir + self.args.pose_rep + "/" + f_name + ext
+            pose_each_file = []
+            trans_each_file = []
+            shape_each_file = []
+            audio_each_file = []
+            facial_each_file = []
+            word_each_file = []
+            emo_each_file = []
+            sem_each_file = []
+            vid_each_file = []
+            id_pose = f_name #1_wayne_0_1_1
+            
+            logger.info(colored(f"# ---- Building cache for Pose   {id_pose} ---- #", "blue"))
+            if "smplx" in self.args.pose_rep:
+                pose_data = np.load(pose_file, allow_pickle=True)
+                assert 30%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 30'
+                stride = int(30/self.args.pose_fps)
+                pose_each_file = pose_data["poses"][::stride] * self.joint_mask
+                trans_each_file = pose_data["trans"][::stride]
+                shape_each_file = np.repeat(pose_data["betas"].reshape(1, 300), pose_each_file.shape[0], axis=0)
+                if self.args.facial_rep is not None:
+                    logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #")
+                    facial_each_file = pose_data["expressions"][::stride]
+                    if self.args.facial_norm: 
+                        facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial
+                    
+            else:
+                assert 120%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120'
+                stride = int(120/self.args.pose_fps)
+                with open(pose_file, "r") as pose_data:
+                    for j, line in enumerate(pose_data.readlines()):
+                        if j < 431: continue     
+                        if j%stride != 0:continue
+                        data = np.fromstring(line, dtype=float, sep=" ")
+                        rot_data = rc.euler_angles_to_matrix(torch.from_numpy(np.deg2rad(data)).reshape(-1, self.joints,3), "XYZ")
+                        rot_data = rc.matrix_to_axis_angle(rot_data).reshape(-1, self.joints*3) 
+                        rot_data = rot_data.numpy() * self.joint_mask
+                        
+                        pose_each_file.append(rot_data)
+                        trans_each_file.append(data[:3])
+                        
+                pose_each_file = np.array(pose_each_file)
+                # print(pose_each_file.shape)
+                trans_each_file = np.array(trans_each_file)
+                shape_each_file = np.repeat(np.array(-1).reshape(1, 1), pose_each_file.shape[0], axis=0)
+                if self.args.facial_rep is not None:
+                    logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #")
+                    facial_file = pose_file.replace(self.args.pose_rep, self.args.facial_rep).replace("bvh", "json")
+                    assert 60%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120'
+                    stride = int(60/self.args.pose_fps)
+                    if not os.path.exists(facial_file):
+                        logger.warning(f"# ---- file not found for Facial {id_pose}, skip all files with the same id ---- #")
+                        self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index)
+                        continue
+                    with open(facial_file, 'r') as facial_data_file:
+                        facial_data = json.load(facial_data_file)
+                        for j, frame_data in enumerate(facial_data['frames']):
+                            if j%stride != 0:continue
+                            facial_each_file.append(frame_data['weights'])
+                    facial_each_file = np.array(facial_each_file)
+                    if self.args.facial_norm: 
+                        facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial
+                        
+            if self.args.id_rep is not None:
+                vid_each_file = np.repeat(np.array(int(f_name.split("_")[0])-1).reshape(1, 1), pose_each_file.shape[0], axis=0)
+      
+            if self.args.audio_rep is not None:
+                logger.info(f"# ---- Building cache for Audio  {id_pose} and Pose {id_pose} ---- #")
+                audio_file = pose_file.replace(self.args.pose_rep, 'wave16k').replace(ext, ".wav")
+                if not os.path.exists(audio_file):
+                    logger.warning(f"# ---- file not found for Audio  {id_pose}, skip all files with the same id ---- #")
+                    self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index)
+                    continue
+                audio_each_file, sr = librosa.load(audio_file)
+                audio_each_file = librosa.resample(audio_each_file, orig_sr=sr, target_sr=self.args.audio_sr)
+                if self.args.audio_rep == "onset+amplitude":
+                    from numpy.lib import stride_tricks
+                    frame_length = 1024
+                    # hop_length = 512
+                    shape = (audio_each_file.shape[-1] - frame_length + 1, frame_length)
+                    strides = (audio_each_file.strides[-1], audio_each_file.strides[-1])
+                    rolling_view = stride_tricks.as_strided(audio_each_file, shape=shape, strides=strides)
+                    amplitude_envelope = np.max(np.abs(rolling_view), axis=1)
+                    # pad the last frame_length-1 samples
+                    amplitude_envelope = np.pad(amplitude_envelope, (0, frame_length-1), mode='constant', constant_values=amplitude_envelope[-1])
+                    audio_onset_f = librosa.onset.onset_detect(y=audio_each_file, sr=self.args.audio_sr, units='frames')
+                    onset_array = np.zeros(len(audio_each_file), dtype=float)
+                    onset_array[audio_onset_f] = 1.0
+                    # print(amplitude_envelope.shape, audio_each_file.shape, onset_array.shape)
+                    audio_each_file = np.concatenate([amplitude_envelope.reshape(-1, 1), onset_array.reshape(-1, 1)], axis=1)
+                elif self.args.audio_rep == "mfcc":
+                    audio_each_file = librosa.feature.mfcc(audio_each_file, sr=self.args.audio_sr, n_mfcc=13, hop_length=int(self.args.audio_sr/self.args.audio_fps))
+
+                if self.args.audio_norm and self.args.audio_rep == "wave16k": 
+                    audio_each_file = (audio_each_file - self.mean_audio) / self.std_audio
+                    
+            time_offset = 0
+            if self.args.word_rep is not None:
+                logger.info(f"# ---- Building cache for Word   {id_pose} and Pose {id_pose} ---- #")
+                word_file = f"{self.data_dir}{self.args.word_rep}/{id_pose}.TextGrid"
+                if not os.path.exists(word_file):
+                    logger.warning(f"# ---- file not found for Word   {id_pose}, skip all files with the same id ---- #")
+                    self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index)
+                    continue
+                tgrid = tg.TextGrid.fromFile(word_file)
+                if self.args.t_pre_encoder == "bert":
+                    from transformers import AutoTokenizer, BertModel
+                    tokenizer = AutoTokenizer.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True)
+                    model = BertModel.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True).eval()
+                    list_word = []
+                    all_hidden = []
+                    max_len = 400
+                    last = 0
+                    word_token_mapping = []
+                    first = True
+                    for i, word in enumerate(tgrid[0]):
+                        last = i
+                        if (i%max_len != 0) or (i==0):
+                            if word.mark == "":
+                                list_word.append(".")
+                            else:
+                                list_word.append(word.mark)
+                        else:
+                            max_counter = max_len
+                            str_word = ' '.join(map(str, list_word))
+                            if first:
+                                global_len = 0
+                            end = -1
+                            offset_word = []
+                            for k, wordvalue in enumerate(list_word):
+                                start = end+1 
+                                end = start+len(wordvalue)
+                                offset_word.append((start, end))
+                            #print(offset_word)
+                            token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping']
+                            #print(token_scan)
+                            for start, end in offset_word:
+                                sub_mapping = []
+                                for i, (start_t, end_t) in enumerate(token_scan[1:-1]):
+                                    if int(start) <= int(start_t) and int(end_t) <= int(end):
+                                        #print(i+global_len)
+                                        sub_mapping.append(i+global_len)
+                                word_token_mapping.append(sub_mapping)
+                            #print(len(word_token_mapping))
+                            global_len = word_token_mapping[-1][-1] + 1    
+                            list_word = []
+                            if word.mark == "":
+                                list_word.append(".")
+                            else:
+                                list_word.append(word.mark)
+                            
+                            with torch.no_grad():
+                                inputs = tokenizer(str_word, return_tensors="pt")
+                                outputs = model(**inputs)
+                                last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :]
+                            all_hidden.append(last_hidden_states)
+                     
+                    #list_word = list_word[:10]
+                    if list_word == []:
+                        pass
+                    else:
+                        if first: 
+                            global_len = 0
+                        str_word = ' '.join(map(str, list_word))
+                        end = -1
+                        offset_word = []
+                        for k, wordvalue in enumerate(list_word):
+                            start = end+1 
+                            end = start+len(wordvalue)
+                            offset_word.append((start, end))
+                        #print(offset_word)
+                        token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping']
+                        #print(token_scan)
+                        for start, end in offset_word:
+                            sub_mapping = []
+                            for i, (start_t, end_t) in enumerate(token_scan[1:-1]):
+                                if int(start) <= int(start_t) and int(end_t) <= int(end):
+                                    sub_mapping.append(i+global_len)
+                                    #print(sub_mapping)
+                            word_token_mapping.append(sub_mapping)
+                        #print(len(word_token_mapping))
+                        with torch.no_grad():
+                            inputs = tokenizer(str_word, return_tensors="pt")
+                            outputs = model(**inputs)
+                            last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :]
+                        all_hidden.append(last_hidden_states)
+                    last_hidden_states = np.concatenate(all_hidden, axis=0)
+            
+                for i in range(pose_each_file.shape[0]):
+                    found_flag = False
+                    current_time = i/self.args.pose_fps + time_offset
+                    j_last = 0
+                    for j, word in enumerate(tgrid[0]): 
+                        word_n, word_s, word_e = word.mark, word.minTime, word.maxTime
+                        if word_s<=current_time and current_time<=word_e:
+                            if self.args.word_cache and self.args.t_pre_encoder == 'bert':
+                                mapping_index = word_token_mapping[j]
+                                #print(mapping_index, word_s, word_e)
+                                s_t = np.linspace(word_s, word_e, len(mapping_index)+1)
+                                #print(s_t)
+                                for tt, t_sep in enumerate(s_t[1:]):
+                                    if current_time <= t_sep:
+                                        #if len(mapping_index) > 1: print(mapping_index[tt])
+                                        word_each_file.append(last_hidden_states[mapping_index[tt]])
+                                        break
+                            else:
+                                if word_n == " ":
+                                    word_each_file.append(self.lang_model.PAD_token)
+                                else:
+                                    word_each_file.append(self.lang_model.get_word_index(word_n))
+                            found_flag = True
+                            j_last = j
+                            break
+                        else: continue   
+                    if not found_flag: 
+                        if self.args.word_cache and self.args.t_pre_encoder == 'bert':
+                            word_each_file.append(last_hidden_states[j_last])
+                        else:
+                            word_each_file.append(self.lang_model.UNK_token)
+                word_each_file = np.array(word_each_file)
+                #print(word_each_file.shape)
+                
+            if self.args.emo_rep is not None:
+                logger.info(f"# ---- Building cache for Emo    {id_pose} and Pose {id_pose} ---- #")
+                rtype, start = int(id_pose.split('_')[3]), int(id_pose.split('_')[3])
+                if rtype == 0 or rtype == 2 or rtype == 4 or rtype == 6:
+                    if start >= 1 and start <= 64:
+                        score = 0
+                    elif start >= 65 and start <= 72:
+                        score = 1
+                    elif start >= 73 and start <= 80:
+                        score = 2
+                    elif start >= 81 and start <= 86:
+                        score = 3
+                    elif start >= 87 and start <= 94:
+                        score = 4
+                    elif start >= 95 and start <= 102:
+                        score = 5
+                    elif start >= 103 and start <= 110:
+                        score = 6
+                    elif start >= 111 and start <= 118:
+                        score = 7
+                    else: pass
+                else:
+                    # you may denote as unknown in the future
+                    score = 0
+                emo_each_file = np.repeat(np.array(score).reshape(1, 1), pose_each_file.shape[0], axis=0)    
+                #print(emo_each_file)
+                
+            if self.args.sem_rep is not None:
+                logger.info(f"# ---- Building cache for Sem    {id_pose} and Pose {id_pose} ---- #")
+                sem_file = f"{self.data_dir}{self.args.sem_rep}/{id_pose}.txt" 
+                sem_all = pd.read_csv(sem_file, 
+                    sep='\t', 
+                    names=["name", "start_time", "end_time", "duration", "score", "keywords"])
+                # we adopt motion-level semantic score here. 
+                for i in range(pose_each_file.shape[0]):
+                    found_flag = False
+                    for j, (start, end, score) in enumerate(zip(sem_all['start_time'],sem_all['end_time'], sem_all['score'])):
+                        current_time = i/self.args.pose_fps + time_offset
+                        if start<=current_time and current_time<=end: 
+                            sem_each_file.append(score)
+                            found_flag=True
+                            break
+                        else: continue 
+                    if not found_flag: sem_each_file.append(0.)
+                sem_each_file = np.array(sem_each_file)
+                #print(sem_each_file)
+            
+            filtered_result = self._sample_from_clip(
+                dst_lmdb_env,
+                audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file,
+                vid_each_file, emo_each_file, sem_each_file,
+                disable_filtering, clean_first_seconds, clean_final_seconds, is_test,
+                ) 
+            for type in filtered_result.keys():
+                n_filtered_out[type] += filtered_result[type]
+                                
+        with dst_lmdb_env.begin() as txn:
+            logger.info(colored(f"no. of samples: {txn.stat()['entries']}", "cyan"))
+            n_total_filtered = 0
+            for type, n_filtered in n_filtered_out.items():
+                logger.info("{}: {}".format(type, n_filtered))
+                n_total_filtered += n_filtered
+            logger.info(colored("no. of excluded samples: {} ({:.1f}%)".format(
+                n_total_filtered, 100 * n_total_filtered / (txn.stat()["entries"] + n_total_filtered)), "cyan"))
+        dst_lmdb_env.sync()
+        dst_lmdb_env.close()
+    
+    def _sample_from_clip(
+        self, dst_lmdb_env, audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file,
+        vid_each_file, emo_each_file, sem_each_file,
+        disable_filtering, clean_first_seconds, clean_final_seconds, is_test,
+        ):
+        """
+        for data cleaning, we ignore the data for first and final n s
+        for test, we return all data 
+        """
+        # audio_start = int(self.alignment[0] * self.args.audio_fps)
+        # pose_start = int(self.alignment[1] * self.args.pose_fps)
+        #logger.info(f"before: {audio_each_file.shape} {pose_each_file.shape}")
+        # audio_each_file = audio_each_file[audio_start:]
+        # pose_each_file = pose_each_file[pose_start:]
+        # trans_each_file = 
+        #logger.info(f"after alignment: {audio_each_file.shape} {pose_each_file.shape}")
+        #print(pose_each_file.shape)
+        round_seconds_skeleton = pose_each_file.shape[0] // self.args.pose_fps  # assume 1500 frames / 15 fps = 100 s
+        #print(round_seconds_skeleton)
+        if audio_each_file != []:
+            round_seconds_audio = len(audio_each_file) // self.args.audio_fps # assume 16,000,00 / 16,000 = 100 s
+            if facial_each_file != []:
+                round_seconds_facial = facial_each_file.shape[0] // self.args.pose_fps
+                logger.info(f"audio: {round_seconds_audio}s, pose: {round_seconds_skeleton}s, facial: {round_seconds_facial}s")
+                round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton, round_seconds_facial)
+                max_round = max(round_seconds_audio, round_seconds_skeleton, round_seconds_facial)
+                if round_seconds_skeleton != max_round: 
+                    logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s")  
+            else:
+                logger.info(f"pose: {round_seconds_skeleton}s, audio: {round_seconds_audio}s")
+                round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton)
+                max_round = max(round_seconds_audio, round_seconds_skeleton)
+                if round_seconds_skeleton != max_round: 
+                    logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s")
+        
+        clip_s_t, clip_e_t = clean_first_seconds, round_seconds_skeleton - clean_final_seconds # assume [10, 90]s
+        clip_s_f_audio, clip_e_f_audio = self.args.audio_fps * clip_s_t, clip_e_t * self.args.audio_fps # [160,000,90*160,000]
+        clip_s_f_pose, clip_e_f_pose = clip_s_t * self.args.pose_fps, clip_e_t * self.args.pose_fps # [150,90*15]
+        
+        
+        for ratio in self.args.multi_length_training:
+            if is_test:# stride = length for test
+                cut_length = clip_e_f_pose - clip_s_f_pose
+                self.args.stride = cut_length
+                self.max_length = cut_length
+            else:
+                self.args.stride = int(ratio*self.ori_stride)
+                cut_length = int(self.ori_length*ratio)
+                
+            num_subdivision = math.floor((clip_e_f_pose - clip_s_f_pose - cut_length) / self.args.stride) + 1
+            logger.info(f"pose from frame {clip_s_f_pose} to {clip_e_f_pose}, length {cut_length}")
+            logger.info(f"{num_subdivision} clips is expected with stride {self.args.stride}")
+            
+            if audio_each_file != []:
+                audio_short_length = math.floor(cut_length / self.args.pose_fps * self.args.audio_fps)
+                """
+                for audio sr = 16000, fps = 15, pose_length = 34, 
+                audio short length = 36266.7 -> 36266
+                this error is fine.
+                """
+                logger.info(f"audio from frame {clip_s_f_audio} to {clip_e_f_audio}, length {audio_short_length}")
+             
+            n_filtered_out = defaultdict(int)
+            sample_pose_list = []
+            sample_audio_list = []
+            sample_facial_list = []
+            sample_shape_list = []
+            sample_word_list = []
+            sample_emo_list = []
+            sample_sem_list = []
+            sample_vid_list = []
+            sample_trans_list = []
+           
+            for i in range(num_subdivision): # cut into around 2s chip, (self npose)
+                start_idx = clip_s_f_pose + i * self.args.stride
+                fin_idx = start_idx + cut_length 
+                sample_pose = pose_each_file[start_idx:fin_idx]
+                sample_trans = trans_each_file[start_idx:fin_idx]
+                sample_shape = shape_each_file[start_idx:fin_idx]
+                # print(sample_pose.shape)
+                if self.args.audio_rep is not None:
+                    audio_start = clip_s_f_audio + math.floor(i * self.args.stride * self.args.audio_fps / self.args.pose_fps)
+                    audio_end = audio_start + audio_short_length
+                    sample_audio = audio_each_file[audio_start:audio_end]
+                else:
+                    sample_audio = np.array([-1])
+                sample_facial = facial_each_file[start_idx:fin_idx] if self.args.facial_rep is not None else np.array([-1])
+                sample_word = word_each_file[start_idx:fin_idx] if self.args.word_rep is not None else np.array([-1])
+                sample_emo = emo_each_file[start_idx:fin_idx] if self.args.emo_rep is not None else np.array([-1])
+                sample_sem = sem_each_file[start_idx:fin_idx] if self.args.sem_rep is not None else np.array([-1])
+                sample_vid = vid_each_file[start_idx:fin_idx] if self.args.id_rep is not None else np.array([-1])
+                
+                if sample_pose.any() != None:
+                    # filtering motion skeleton data
+                    sample_pose, filtering_message = MotionPreprocessor(sample_pose).get()
+                    is_correct_motion = (sample_pose != [])
+                    if is_correct_motion or disable_filtering:
+                        sample_pose_list.append(sample_pose)
+                        sample_audio_list.append(sample_audio)
+                        sample_facial_list.append(sample_facial)
+                        sample_shape_list.append(sample_shape)
+                        sample_word_list.append(sample_word)
+                        sample_vid_list.append(sample_vid)
+                        sample_emo_list.append(sample_emo)
+                        sample_sem_list.append(sample_sem)
+                        sample_trans_list.append(sample_trans)
+                    else:
+                        n_filtered_out[filtering_message] += 1
+
+            if len(sample_pose_list) > 0:
+                with dst_lmdb_env.begin(write=True) as txn:
+                    for pose, audio, facial, shape, word, vid, emo, sem, trans in zip(
+                        sample_pose_list,
+                        sample_audio_list,
+                        sample_facial_list,
+                        sample_shape_list,
+                        sample_word_list,
+                        sample_vid_list,
+                        sample_emo_list,
+                        sample_sem_list,
+                        sample_trans_list,):
+                        k = "{:005}".format(self.n_out_samples).encode("ascii")
+                        v = [pose, audio, facial, shape, word, emo, sem, vid, trans]
+                        v = pyarrow.serialize(v).to_buffer()
+                        txn.put(k, v)
+                        self.n_out_samples += 1
+        return n_filtered_out
+
+    def __getitem__(self, idx):
+        with self.lmdb_env.begin(write=False) as txn:
+            key = "{:005}".format(idx).encode("ascii")
+            sample = txn.get(key)
+            sample = pyarrow.deserialize(sample)
+            tar_pose, in_audio, in_facial, in_shape, in_word, emo, sem, vid, trans = sample
+            #print(in_shape)
+            #vid = torch.from_numpy(vid).int()
+            emo = torch.from_numpy(emo).int()
+            sem = torch.from_numpy(sem).float() 
+            in_audio = torch.from_numpy(in_audio).float() 
+            in_word = torch.from_numpy(in_word).float() if self.args.word_cache else torch.from_numpy(in_word).int() 
+            if self.loader_type == "test":
+                tar_pose = torch.from_numpy(tar_pose).float()
+                trans = torch.from_numpy(trans).float()
+                in_facial = torch.from_numpy(in_facial).float()
+                vid = torch.from_numpy(vid).float()
+                in_shape = torch.from_numpy(in_shape).float()
+            else:
+                in_shape = torch.from_numpy(in_shape).reshape((in_shape.shape[0], -1)).float()
+                trans = torch.from_numpy(trans).reshape((trans.shape[0], -1)).float()
+                vid = torch.from_numpy(vid).reshape((vid.shape[0], -1)).float()
+                tar_pose = torch.from_numpy(tar_pose).reshape((tar_pose.shape[0], -1)).float()
+                in_facial = torch.from_numpy(in_facial).reshape((in_facial.shape[0], -1)).float()
+            return {"pose":tar_pose, "audio":in_audio, "facial":in_facial, "beta": in_shape, "word":in_word, "id":vid, "emo":emo, "sem":sem, "trans":trans}
+
+         
+class MotionPreprocessor:
+    def __init__(self, skeletons):
+        self.skeletons = skeletons
+        #self.mean_pose = mean_pose
+        self.filtering_message = "PASS"
+
+    def get(self):
+        assert (self.skeletons is not None)
+
+        # filtering
+        if self.skeletons != []:
+            if self.check_pose_diff():
+                self.skeletons = []
+                self.filtering_message = "pose"
+            # elif self.check_spine_angle():
+            #     self.skeletons = []
+            #     self.filtering_message = "spine angle"
+            # elif self.check_static_motion():
+            #     self.skeletons = []
+            #     self.filtering_message = "motion"
+
+        # if self.skeletons != []:
+        #     self.skeletons = self.skeletons.tolist()
+        #     for i, frame in enumerate(self.skeletons):
+        #         assert not np.isnan(self.skeletons[i]).any()  # missing joints
+
+        return self.skeletons, self.filtering_message
+
+    def check_static_motion(self, verbose=True):
+        def get_variance(skeleton, joint_idx):
+            wrist_pos = skeleton[:, joint_idx]
+            variance = np.sum(np.var(wrist_pos, axis=0))
+            return variance
+
+        left_arm_var = get_variance(self.skeletons, 6)
+        right_arm_var = get_variance(self.skeletons, 9)
+
+        th = 0.0014  # exclude 13110
+        # th = 0.002  # exclude 16905
+        if left_arm_var < th and right_arm_var < th:
+            if verbose:
+                print("skip - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var))
+            return True
+        else:
+            if verbose:
+                print("pass - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var))
+            return False
+
+
+    def check_pose_diff(self, verbose=False):
+#         diff = np.abs(self.skeletons - self.mean_pose) # 186*1
+#         diff = np.mean(diff)
+
+#         # th = 0.017
+#         th = 0.02 #0.02  # exclude 3594
+#         if diff < th:
+#             if verbose:
+#                 print("skip - check_pose_diff {:.5f}".format(diff))
+#             return True
+# #         th = 3.5 #0.02  # exclude 3594
+# #         if 3.5 < diff < 5:
+# #             if verbose:
+# #                 print("skip - check_pose_diff {:.5f}".format(diff))
+# #             return True
+#         else:
+#             if verbose:
+#                 print("pass - check_pose_diff {:.5f}".format(diff))
+        return False
+
+
+    def check_spine_angle(self, verbose=True):
+        def angle_between(v1, v2):
+            v1_u = v1 / np.linalg.norm(v1)
+            v2_u = v2 / np.linalg.norm(v2)
+            return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0))
+
+        angles = []
+        for i in range(self.skeletons.shape[0]):
+            spine_vec = self.skeletons[i, 1] - self.skeletons[i, 0]
+            angle = angle_between(spine_vec, [0, -1, 0])
+            angles.append(angle)
+
+        if np.rad2deg(max(angles)) > 30 or np.rad2deg(np.mean(angles)) > 20:  # exclude 4495
+        # if np.rad2deg(max(angles)) > 20:  # exclude 8270
+            if verbose:
+                print("skip - check_spine_angle {:.5f}, {:.5f}".format(max(angles), np.mean(angles)))
+            return True
+        else:
+            if verbose:
+                print("pass - check_spine_angle {:.5f}".format(max(angles)))
+            return False

dataloaders/build_vocab.py

@@ -0,0 +1,199 @@
+import numpy as np
+import glob
+import os
+import pickle
+import lmdb
+#import pyarrow
+import fasttext
+from loguru import logger
+from scipy import linalg
+
+
+class Vocab:
+    PAD_token = 0
+    SOS_token = 1
+    EOS_token = 2
+    UNK_token = 3
+
+    def __init__(self, name, insert_default_tokens=True):
+        self.name = name
+        self.trimmed = False
+        self.word_embedding_weights = None
+        self.reset_dictionary(insert_default_tokens)
+
+    def reset_dictionary(self, insert_default_tokens=True):
+        self.word2index = {}
+        self.word2count = {}
+        if insert_default_tokens:
+            self.index2word = {self.PAD_token: "<PAD>", self.SOS_token: "<SOS>",
+                               self.EOS_token: "<EOS>", self.UNK_token: "<UNK>"}
+        else:
+            self.index2word = {self.UNK_token: "<UNK>"}
+        self.n_words = len(self.index2word)  # count default tokens
+
+    def index_word(self, word):
+        if word not in self.word2index:
+            self.word2index[word] = self.n_words
+            self.word2count[word] = 1
+            self.index2word[self.n_words] = word
+            self.n_words += 1
+        else:
+            self.word2count[word] += 1
+
+    def add_vocab(self, other_vocab):
+        for word, _ in other_vocab.word2count.items():
+            self.index_word(word)
+
+    # remove words below a certain count threshold
+    def trim(self, min_count):
+        if self.trimmed:
+            return
+        self.trimmed = True
+
+        keep_words = []
+
+        for k, v in self.word2count.items():
+            if v >= min_count:
+                keep_words.append(k)
+
+        print('    word trimming, kept %s / %s = %.4f' % (
+            len(keep_words), len(self.word2index), len(keep_words) / len(self.word2index)
+        ))
+
+        # reinitialize dictionary
+        self.reset_dictionary()
+        for word in keep_words:
+            self.index_word(word)
+
+    def get_word_index(self, word):
+        if word in self.word2index:
+            return self.word2index[word]
+        else:
+            return self.UNK_token
+
+    def load_word_vectors(self, pretrained_path, embedding_dim=300):
+        print("  loading word vectors from '{}'...".format(pretrained_path))
+
+        # initialize embeddings to random values for special words
+        init_sd = 1 / np.sqrt(embedding_dim)
+        weights = np.random.normal(0, scale=init_sd, size=[self.n_words, embedding_dim])
+        weights = weights.astype(np.float32)
+
+        # read word vectors
+        word_model = fasttext.load_model(pretrained_path)
+        for word, id in self.word2index.items():
+            vec = word_model.get_word_vector(word)
+            weights[id] = vec
+        self.word_embedding_weights = weights
+
+    def __get_embedding_weight(self, pretrained_path, embedding_dim=300):
+        """ function modified from http://ronny.rest/blog/post_2017_08_04_glove/ """
+        print("Loading word embedding '{}'...".format(pretrained_path))
+        cache_path = pretrained_path
+        weights = None
+
+        # use cached file if it exists
+        if os.path.exists(cache_path):  #
+            with open(cache_path, 'rb') as f:
+                print('  using cached result from {}'.format(cache_path))
+                weights = pickle.load(f)
+                if weights.shape != (self.n_words, embedding_dim):
+                    logging.warning('  failed to load word embedding weights. reinitializing...')
+                    weights = None
+
+        if weights is None:
+            # initialize embeddings to random values for special and OOV words
+            init_sd = 1 / np.sqrt(embedding_dim)
+            weights = np.random.normal(0, scale=init_sd, size=[self.n_words, embedding_dim])
+            weights = weights.astype(np.float32)
+
+            with open(pretrained_path, encoding="utf-8", mode="r") as textFile:
+                num_embedded_words = 0
+                for line_raw in textFile:
+                    # extract the word, and embeddings vector
+                    line = line_raw.split()
+                    try:
+                        word, vector = (line[0], np.array(line[1:], dtype=np.float32))
+                        # if word == 'love':  # debugging
+                        #     print(word, vector)
+
+                        # if it is in our vocab, then update the corresponding weights
+                        id = self.word2index.get(word, None)
+                        if id is not None:
+                            weights[id] = vector
+                            num_embedded_words += 1
+                    except ValueError:
+                        print('  parsing error at {}...'.format(line_raw[:50]))
+                        continue
+                print('  {} / {} word vectors are found in the embedding'.format(num_embedded_words, len(self.word2index)))
+
+                with open(cache_path, 'wb') as f:
+                    pickle.dump(weights, f)
+        return weights
+
+
+def build_vocab(name, data_path, cache_path, word_vec_path=None, feat_dim=None):
+    print('  building a language model...')
+    #if not os.path.exists(cache_path):
+    lang_model = Vocab(name)
+    print('    indexing words from {}'.format(data_path))
+    index_words_from_textgrid(lang_model, data_path)
+
+    if word_vec_path is not None:
+        lang_model.load_word_vectors(word_vec_path, feat_dim)
+    else:
+        print('    loaded from {}'.format(cache_path))
+        with open(cache_path, 'rb') as f:
+            lang_model = pickle.load(f)
+        if word_vec_path is None:
+            lang_model.word_embedding_weights = None
+        elif lang_model.word_embedding_weights.shape[0] != lang_model.n_words:
+            logging.warning('    failed to load word embedding weights. check this')
+            assert False
+
+    with open(cache_path, 'wb') as f:
+        pickle.dump(lang_model, f)
+
+
+    return lang_model
+
+
+def index_words(lang_model, data_path):
+    #index words form text
+    with open(data_path, "r") as f:
+        for line in f.readlines():
+            line = line.replace(",", " ")
+            line = line.replace(".", " ")
+            line = line.replace("?", " ")
+            line = line.replace("!", " ")
+            for word in line.split(): 
+                lang_model.index_word(word)
+    print('    indexed %d words' % lang_model.n_words)
+
+def index_words_from_textgrid(lang_model, data_path):
+    import textgrid as tg
+    from tqdm import tqdm
+    #trainvaltest=os.listdir(data_path)
+    # for loadtype in trainvaltest:
+    #     if "." in loadtype: continue #ignore .ipynb_checkpoints
+    texts = os.listdir(data_path+"/textgrid/")
+    #print(texts)
+    for textfile in tqdm(texts):
+        tgrid = tg.TextGrid.fromFile(data_path+"/textgrid/"+textfile)
+        for word in tgrid[0]:
+            word_n, word_s, word_e = word.mark, word.minTime, word.maxTime
+            word_n = word_n.replace(",", " ")
+            word_n = word_n.replace(".", " ")
+            word_n = word_n.replace("?", " ")
+            word_n = word_n.replace("!", " ")
+            #print(word_n)
+            lang_model.index_word(word_n)
+    print('    indexed %d words' % lang_model.n_words) 
+    print(lang_model.word2index, lang_model.word2count)
+    
+if __name__ == "__main__":
+    # 11195 for all, 5793 for 4 speakers
+    # build_vocab("beat_english_15_141", "/home/ma-user/work/datasets/beat_cache/beat_english_15_141/", "/home/ma-user/work/datasets/beat_cache/beat_english_15_141/vocab.pkl", "/home/ma-user/work/datasets/cc.en.300.bin", 300)
+    build_vocab("beat_chinese_v1.0.0", "/data/datasets/beat_chinese_v1.0.0/", "/data/datasets/beat_chinese_v1.0.0/weights/vocab.pkl", "/home/ma-user/work/cc.zh.300.bin", 300)
+    
+    

dataloaders/data_tools.py

@@ -0,0 +1,1756 @@
+import numpy as np
+import glob
+import os
+import pickle
+import lmdb
+#import pyarrow
+import fasttext
+from loguru import logger
+from scipy import linalg
+from .pymo.parsers import BVHParser
+from .pymo.viz_tools import *
+from .pymo.preprocessing import *
+
+
+
+
+# pose version fpsxx_trinity/japanese_joints(_xxx)
+joints_list = {
+    "trinity_joints":{
+        'Hips':         [6,6],
+        'Spine':        [3,9],
+        'Spine1':       [3,12],
+        'Spine2':       [3,15],
+        'Spine3':       [3,18],
+        'Neck':         [3,21],
+        'Neck1':        [3,24],
+        'Head':         [3,27],
+        'RShoulder':    [3,30], 
+        'RArm':         [3,33],
+        'RArm1':        [3,36],
+        'RHand':        [3,39],
+        'RHandT1':      [3,42],
+        'RHandT2':      [3,45],
+        'RHandT3':      [3,48],
+        'RHandI1':      [3,51],
+        'RHandI2':      [3,54],
+        'RHandI3':      [3,57],
+        'RHandM1':      [3,60],
+        'RHandM2':      [3,63],
+        'RHandM3':      [3,66],
+        'RHandR1':      [3,69],
+        'RHandR2':      [3,72],
+        'RHandR3':      [3,75],
+        'RHandP1':      [3,78],
+        'RHandP2':      [3,81],
+        'RHandP3':      [3,84],
+        'LShoulder':    [3,87], 
+        'LArm':         [3,90],
+        'LArm1':        [3,93],
+        'LHand':        [3,96], 
+        'LHandT1':      [3,99],
+        'LHandT2':      [3,102],
+        'LHandT3':      [3,105],
+        'LHandI1':      [3,108],
+        'LHandI2':      [3,111],
+        'LHandI3':      [3,114],
+        'LHandM1':      [3,117],
+        'LHandM2':      [3,120],
+        'LHandM3':      [3,123],
+        'LHandR1':      [3,126],
+        'LHandR2':      [3,129],
+        'LHandR3':      [3,132],
+        'LHandP1':      [3,135],
+        'LHandP2':      [3,138],
+        'LHandP3':      [3,141],
+        'RUpLeg':       [3,144],
+        'RLeg':         [3,147],
+        'RFoot':        [3,150],
+        'RFootF':       [3,153],
+        'RToeBase':     [3,156],
+        'LUpLeg':       [3,159],
+        'LLeg':         [3,162],
+        'LFoot':        [3,165],
+        'LFootF':       [3,168],
+        'LToeBase':     [3,171],},
+    "trinity_joints_123":{ 
+        'Spine':       3 ,
+        'Neck':        3 ,
+        'Neck1':       3 ,
+        'RShoulder':   3 , 
+        'RArm':        3 ,
+        'RArm1':       3 ,
+        'RHand':       3 ,
+        'RHandT1':     3 ,
+        'RHandT2':     3 ,
+        'RHandT3':     3 ,
+        'RHandI1':     3 ,
+        'RHandI2':     3 ,
+        'RHandI3':     3 ,
+        'RHandM1':     3 ,
+        'RHandM2':     3 ,
+        'RHandM3':     3 ,
+        'RHandR1':     3 ,
+        'RHandR2':     3 ,
+        'RHandR3':     3 ,
+        'RHandP1':     3 ,
+        'RHandP2':     3 ,
+        'RHandP3':     3 ,
+        'LShoulder':   3 , 
+        'LArm':        3 ,
+        'LArm1':       3 ,
+        'LHand':       3 ,
+        'LHandT1':     3 ,
+        'LHandT2':     3 ,
+        'LHandT3':     3 ,
+        'LHandI1':     3 ,
+        'LHandI2':     3 ,
+        'LHandI3':     3 ,
+        'LHandM1':     3 ,
+        'LHandM2':     3 ,
+        'LHandM3':     3 ,
+        'LHandR1':     3 ,
+        'LHandR2':     3 ,
+        'LHandR3':     3 ,
+        'LHandP1':     3 ,
+        'LHandP2':     3 ,
+        'LHandP3':     3 ,},
+    "trinity_joints_168":{
+        'Hips':        3 , 
+        'Spine':       3 ,
+        'Spine1':      3 ,
+        'Spine2':      3 ,
+        'Spine3':      3 ,
+        'Neck':        3 ,
+        'Neck1':       3 ,
+        'Head':        3 ,
+        'RShoulder':   3 , 
+        'RArm':        3 ,
+        'RArm1':       3 ,
+        'RHand':       3 ,
+        'RHandT1':     3 ,
+        'RHandT2':     3 ,
+        'RHandT3':     3 ,
+        'RHandI1':     3 ,
+        'RHandI2':     3 ,
+        'RHandI3':     3 ,
+        'RHandM1':     3 ,
+        'RHandM2':     3 ,
+        'RHandM3':     3 ,
+        'RHandR1':     3 ,
+        'RHandR2':     3 ,
+        'RHandR3':     3 ,
+        'RHandP1':     3 ,
+        'RHandP2':     3 ,
+        'RHandP3':     3 ,
+        'LShoulder':   3 , 
+        'LArm':        3 ,
+        'LArm1':       3 ,
+        'LHand':       3 ,
+        'LHandT1':     3 ,
+        'LHandT2':     3 ,
+        'LHandT3':     3 ,
+        'LHandI1':     3 ,
+        'LHandI2':     3 ,
+        'LHandI3':     3 ,
+        'LHandM1':     3 ,
+        'LHandM2':     3 ,
+        'LHandM3':     3 ,
+        'LHandR1':     3 ,
+        'LHandR2':     3 ,
+        'LHandR3':     3 ,
+        'LHandP1':     3 ,
+        'LHandP2':     3 ,
+        'LHandP3':     3 ,
+        'RUpLeg':      3 ,
+        'RLeg':        3 ,
+        'RFoot':       3 ,
+        'RFootF':      3 ,
+        'RToeBase':    3 ,
+        'LUpLeg':      3 ,
+        'LLeg':        3 ,
+        'LFoot':       3 ,
+        'LFootF':      3 ,
+        'LToeBase':    3 ,},
+    "trinity_joints_138":{
+        "Hips":        3 ,
+        'Spine':       3 ,
+        'Spine1':      3 ,
+        'Spine2':      3 ,
+        'Spine3':      3 ,
+        'Neck':        3 ,
+        'Neck1':       3 ,
+        'Head':        3 ,
+        'RShoulder':   3 , 
+        'RArm':        3 ,
+        'RArm1':       3 ,
+        'RHand':       3 ,
+        'RHandT1':     3 ,
+        'RHandT2':     3 ,
+        'RHandT3':     3 ,
+        'RHandI1':     3 ,
+        'RHandI2':     3 ,
+        'RHandI3':     3 ,
+        'RHandM1':     3 ,
+        'RHandM2':     3 ,
+        'RHandM3':     3 ,
+        'RHandR1':     3 ,
+        'RHandR2':     3 ,
+        'RHandR3':     3 ,
+        'RHandP1':     3 ,
+        'RHandP2':     3 ,
+        'RHandP3':     3 ,
+        'LShoulder':   3 , 
+        'LArm':        3 ,
+        'LArm1':       3 ,
+        'LHand':       3 ,
+        'LHandT1':     3 ,
+        'LHandT2':     3 ,
+        'LHandT3':     3 ,
+        'LHandI1':     3 ,
+        'LHandI2':     3 ,
+        'LHandI3':     3 ,
+        'LHandM1':     3 ,
+        'LHandM2':     3 ,
+        'LHandM3':     3 ,
+        'LHandR1':     3 ,
+        'LHandR2':     3 ,
+        'LHandR3':     3 ,
+        'LHandP1':     3 ,
+        'LHandP2':     3 ,
+        'LHandP3':     3 ,},
+    "beat_smplx_joints": {
+        'pelvis':         [3,3],
+        'left_hip':        [3,6],
+        'right_hip':       [3,9],
+        'spine1':       [3,12],
+        'left_knee':       [3,15],
+        'right_knee':         [3,18],
+        'spine2':        [3,21],
+        'left_ankle':         [3,24],
+        'right_ankle':      [3,27],
+
+        'spine3':    [3,30], 
+        'left_foot':         [3,33],
+        'right_foot':        [3,36],
+        'neck':        [3,39],    
+        'left_collar':      [3,42],
+        'right_collar':      [3,45],
+        'head':      [3,48],
+        'left_shoulder':      [3,51],
+
+        'right_shoulder':       [3,54],
+        'left_elbow':      [3,57],
+        'right_elbow':      [3,60],
+        'left_wrist':      [3,63],
+        'right_wrist':      [3,66],
+
+        'jaw':       [3,69],
+        'left_eye_smplhf':      [3,72],
+        'right_eye_smplhf':      [3,75],
+        'left_index1':      [3,78],
+        'left_index2':      [3,81],
+
+        'left_index3':       [3,84],
+        'left_middle1':      [3,87],
+        'left_middle2':      [3,90],
+        'left_middle3':      [3,93],
+        'left_pinky1':      [3,96],
+
+        'left_pinky2':      [3,99],
+        'left_pinky3':      [3,102],
+        'left_ring1':      [3,105],
+        'left_ring2':      [3,108],
+
+        'left_ring3':    [3,111], 
+        'left_thumb1':         [3,114],
+        'left_thumb2':        [3,117],
+        'left_thumb3':        [3,120],    
+        'right_index1':      [3,123],
+        'right_index2':      [3,126],
+        'right_index3':      [3,129],
+        'right_middle1':      [3,132],
+
+        'right_middle2':       [3,135],
+        'right_middle3':      [3,138],
+        'right_pinky1':      [3,141],
+        'right_pinky2':      [3,144],
+        'right_pinky3':      [3,147],
+
+        'right_ring1':       [3,150],
+        'right_ring2':      [3,153],
+        'right_ring3':      [3,156],
+        'right_thumb1':      [3,159],
+        'right_thumb2':      [3,162],
+        'right_thumb3':       [3,165],
+        
+#         'nose':      [3,168],
+#         'right_eye':      [3,171],
+#         'left_eye':      [3,174],
+#         'right_ear':      [3,177],
+
+#         'left_ear':      [3,180],
+#         'left_big_toe':      [3,183],
+#         'left_small_toe':      [3,186],
+#         'left_heel':      [3,189],
+
+#         'right_big_toe':       [3,192],
+#         'right_small_toe':         [3,195],
+#         'right_heel':        [3,198],
+#         'left_thumb':       [3,201],
+#         'left_index':     [3,204],
+#         'left_middle':  [3,207],
+
+#         'left_ring':       [3,210],
+#         'left_pinky':         [3,213],
+#         'right_thumb':        [3,216],
+#         'right_index':       [3,219],
+#         'right_middle':     [3,222],
+#         'right_ring':  [3,225],
+        
+#         'right_pinky':      [3,228],
+#         'right_eye_brow1':      [3,231],
+#         'right_eye_brow2':      [3,234],
+#         'right_eye_brow3':      [3,237],
+
+#         'right_eye_brow4':      [3,240],
+#         'right_eye_brow5':      [3,243],
+#         'left_eye_brow5':      [3,246],
+#         'left_eye_brow4':      [3,249],
+
+#         'left_eye_brow3':       [3,252],
+#         'left_eye_brow2':         [3,255],
+#         'left_eye_brow1':        [3,258],
+#         'nose1':       [3,261],
+#         'nose2':     [3,264],
+#         'nose3':  [3,267],
+
+#         'nose4':       [3,270],
+#         'right_nose_2':         [3,273],
+#         'right_nose_1':        [3,276],
+#         'nose_middle':       [3,279],
+#         'left_nose_1':     [3,282],
+#         'left_nose_2':  [3,285],
+        
+#         'right_eye1':      [3,288],
+#         'right_eye2':      [3,291],
+#         'right_eye3':      [3,294],
+#         'right_eye4':      [3,297],
+
+#         'right_eye5':      [3,300],
+#         'right_eye6':      [3,303],
+#         'left_eye4':      [3,306],
+#         'left_eye3':      [3,309],
+
+#         'left_eye2':       [3,312],
+#         'left_eye1':         [3,315],
+#         'left_eye6':        [3,318],
+#         'left_eye5':       [3,321],
+#         'right_mouth_1':     [3,324],
+#         'right_mouth_2':  [3,327],
+#         'right_mouth_3':       [3,330],
+#         'mouth_top':         [3,333],
+#         'left_mouth_3':        [3,336],
+#         'left_mouth_2':       [3,339],
+#         'left_mouth_1':     [3,342],
+#         'left_mouth_5':  [3,345],
+#         'left_mouth_4':        [3,348],
+#         'mouth_bottom':       [3,351],
+#         'right_mouth_4':     [3,354],
+#         'right_mouth_5':  [3,357],
+#         'right_lip_1':        [3,360],
+#         'right_lip_2':       [3,363],
+#         'lip_top':     [3,366],
+#         'left_lip_2':  [3,369],
+        
+#         'left_lip_1':       [3,372],
+#         'left_lip_3':         [3,375],
+#         'lip_bottom':        [3,378],
+#         'right_lip_3':       [3,381],
+#         'right_contour_1':     [3,384],
+#         'right_contour_2':  [3,387],
+#         'right_contour_3':       [3,390],
+#         'right_contour_4':         [3,393],
+#         'right_contour_5':        [3,396],
+#         'right_contour_6':       [3,399],
+#         'right_contour_7':     [3,402],
+#         'right_contour_8':  [3,405],
+#         'contour_middle':        [3,408],
+#         'left_contour_8':       [3,411],
+#         'left_contour_7':     [3,414],
+#         'left_contour_6':  [3,417],
+#         'left_contour_5':        [3,420],
+#         'left_contour_4':       [3,423],
+#         'left_contour_3':     [3,426],
+#         'left_contour_2':  [3,429],
+#         'left_contour_1':  [3,432],
+    },
+    
+    "beat_smplx_no_eyes": {
+        "pelvis":3,
+        "left_hip":3,
+        "right_hip":3,
+        "spine1":3,
+        "left_knee":3,
+        "right_knee":3,
+        "spine2":3,
+        "left_ankle":3,
+        "right_ankle":3,
+        "spine3":3,
+        "left_foot":3,
+        "right_foot":3,
+        "neck":3,
+        "left_collar":3,
+        "right_collar":3,
+        "head":3,
+        "left_shoulder":3,
+        "right_shoulder":3,
+        "left_elbow":3,
+        "right_elbow":3,
+        "left_wrist":3,
+        "right_wrist":3,
+        "jaw":3,
+        # "left_eye_smplhf":3,
+        # "right_eye_smplhf":3,
+        "left_index1":3,
+        "left_index2":3,
+        "left_index3":3,
+        "left_middle1":3,
+        "left_middle2":3,
+        "left_middle3":3,
+        "left_pinky1":3,
+        "left_pinky2":3,
+        "left_pinky3":3,
+        "left_ring1":3,
+        "left_ring2":3,
+        "left_ring3":3,
+        "left_thumb1":3,
+        "left_thumb2":3,
+        "left_thumb3":3,
+        "right_index1":3,
+        "right_index2":3,
+        "right_index3":3,
+        "right_middle1":3,
+        "right_middle2":3,
+        "right_middle3":3,
+        "right_pinky1":3,
+        "right_pinky2":3,
+        "right_pinky3":3,
+        "right_ring1":3,
+        "right_ring2":3,
+        "right_ring3":3,
+        "right_thumb1":3,
+        "right_thumb2":3,
+        "right_thumb3":3,
+    },
+    
+    "beat_smplx_full": {
+        "pelvis":3,
+        "left_hip":3,
+        "right_hip":3,
+        "spine1":3,
+        "left_knee":3,
+        "right_knee":3,
+        "spine2":3,
+        "left_ankle":3,
+        "right_ankle":3,
+        "spine3":3,
+        "left_foot":3,
+        "right_foot":3,
+        "neck":3,
+        "left_collar":3,
+        "right_collar":3,
+        "head":3,
+        "left_shoulder":3,
+        "right_shoulder":3,
+        "left_elbow":3,
+        "right_elbow":3,
+        "left_wrist":3,
+        "right_wrist":3,
+        "jaw":3,
+        "left_eye_smplhf":3,
+        "right_eye_smplhf":3,
+        "left_index1":3,
+        "left_index2":3,
+        "left_index3":3,
+        "left_middle1":3,
+        "left_middle2":3,
+        "left_middle3":3,
+        "left_pinky1":3,
+        "left_pinky2":3,
+        "left_pinky3":3,
+        "left_ring1":3,
+        "left_ring2":3,
+        "left_ring3":3,
+        "left_thumb1":3,
+        "left_thumb2":3,
+        "left_thumb3":3,
+        "right_index1":3,
+        "right_index2":3,
+        "right_index3":3,
+        "right_middle1":3,
+        "right_middle2":3,
+        "right_middle3":3,
+        "right_pinky1":3,
+        "right_pinky2":3,
+        "right_pinky3":3,
+        "right_ring1":3,
+        "right_ring2":3,
+        "right_ring3":3,
+        "right_thumb1":3,
+        "right_thumb2":3,
+        "right_thumb3":3,
+    },
+
+    "beat_smplx_upall": {
+        # "pelvis":3,
+        # "left_hip":3,
+        # "right_hip":3,
+        "spine1":3,
+        # "left_knee":3,
+        # "right_knee":3,
+        "spine2":3,
+        # "left_ankle":3,
+        # "right_ankle":3,
+        "spine3":3,
+        # "left_foot":3,
+        # "right_foot":3,
+        "neck":3,
+        "left_collar":3,
+        "right_collar":3,
+        "head":3,
+        "left_shoulder":3,
+        "right_shoulder":3,
+        "left_elbow":3,
+        "right_elbow":3,
+        "left_wrist":3,
+        "right_wrist":3,
+        # "jaw":3,
+        # "left_eye_smplhf":3,
+        # "right_eye_smplhf":3,
+        "left_index1":3,
+        "left_index2":3,
+        "left_index3":3,
+        "left_middle1":3,
+        "left_middle2":3,
+        "left_middle3":3,
+        "left_pinky1":3,
+        "left_pinky2":3,
+        "left_pinky3":3,
+        "left_ring1":3,
+        "left_ring2":3,
+        "left_ring3":3,
+        "left_thumb1":3,
+        "left_thumb2":3,
+        "left_thumb3":3,
+        "right_index1":3,
+        "right_index2":3,
+        "right_index3":3,
+        "right_middle1":3,
+        "right_middle2":3,
+        "right_middle3":3,
+        "right_pinky1":3,
+        "right_pinky2":3,
+        "right_pinky3":3,
+        "right_ring1":3,
+        "right_ring2":3,
+        "right_ring3":3,
+        "right_thumb1":3,
+        "right_thumb2":3,
+        "right_thumb3":3,
+    },
+
+    "beat_smplx_upper": {
+        #"pelvis":3,
+        # "left_hip":3,
+        # "right_hip":3,
+        "spine1":3,
+        # "left_knee":3,
+        # "right_knee":3,
+        "spine2":3,
+        # "left_ankle":3,
+        # "right_ankle":3,
+        "spine3":3,
+        # "left_foot":3,
+        # "right_foot":3,
+        "neck":3,
+        "left_collar":3,
+        "right_collar":3,
+        "head":3,
+        "left_shoulder":3,
+        "right_shoulder":3,
+        "left_elbow":3,
+        "right_elbow":3,
+        "left_wrist":3,
+        "right_wrist":3,
+        # "jaw":3,
+        # "left_eye_smplhf":3,
+        # "right_eye_smplhf":3,
+        # "left_index1":3,
+        # "left_index2":3,
+        # "left_index3":3,
+        # "left_middle1":3,
+        # "left_middle2":3,
+        # "left_middle3":3,
+        # "left_pinky1":3,
+        # "left_pinky2":3,
+        # "left_pinky3":3,
+        # "left_ring1":3,
+        # "left_ring2":3,
+        # "left_ring3":3,
+        # "left_thumb1":3,
+        # "left_thumb2":3,
+        # "left_thumb3":3,
+        # "right_index1":3,
+        # "right_index2":3,
+        # "right_index3":3,
+        # "right_middle1":3,
+        # "right_middle2":3,
+        # "right_middle3":3,
+        # "right_pinky1":3,
+        # "right_pinky2":3,
+        # "right_pinky3":3,
+        # "right_ring1":3,
+        # "right_ring2":3,
+        # "right_ring3":3,
+        # "right_thumb1":3,
+        # "right_thumb2":3,
+        # "right_thumb3":3,
+    },
+
+        "beat_smplx_hands": {
+        #"pelvis":3,
+        # "left_hip":3,
+        # "right_hip":3,
+        # "spine1":3,
+        # "left_knee":3,
+        # "right_knee":3,
+        # "spine2":3,
+        # "left_ankle":3,
+        # "right_ankle":3,
+        # "spine3":3,
+        # "left_foot":3,
+        # "right_foot":3,
+        # "neck":3,
+        # "left_collar":3,
+        # "right_collar":3,
+        # "head":3,
+        # "left_shoulder":3,
+        # "right_shoulder":3,
+        # "left_elbow":3,
+        # "right_elbow":3,
+        # "left_wrist":3,
+        # "right_wrist":3,
+        # "jaw":3,
+        # "left_eye_smplhf":3,
+        # "right_eye_smplhf":3,
+        "left_index1":3,
+        "left_index2":3,
+        "left_index3":3,
+        "left_middle1":3,
+        "left_middle2":3,
+        "left_middle3":3,
+        "left_pinky1":3,
+        "left_pinky2":3,
+        "left_pinky3":3,
+        "left_ring1":3,
+        "left_ring2":3,
+        "left_ring3":3,
+        "left_thumb1":3,
+        "left_thumb2":3,
+        "left_thumb3":3,
+        "right_index1":3,
+        "right_index2":3,
+        "right_index3":3,
+        "right_middle1":3,
+        "right_middle2":3,
+        "right_middle3":3,
+        "right_pinky1":3,
+        "right_pinky2":3,
+        "right_pinky3":3,
+        "right_ring1":3,
+        "right_ring2":3,
+        "right_ring3":3,
+        "right_thumb1":3,
+        "right_thumb2":3,
+        "right_thumb3":3,
+    },
+
+    "beat_smplx_lower": {
+        "pelvis":3,
+        "left_hip":3,
+        "right_hip":3,
+        # "spine1":3,
+        "left_knee":3,
+        "right_knee":3,
+        # "spine2":3,
+        "left_ankle":3,
+        "right_ankle":3,
+        # "spine3":3,
+        "left_foot":3,
+        "right_foot":3,
+        # "neck":3,
+        # "left_collar":3,
+        # "right_collar":3,
+        # "head":3,
+        # "left_shoulder":3,
+        # "right_shoulder":3,
+        # "left_elbow":3,
+        # "right_elbow":3,
+        # "left_wrist":3,
+        # "right_wrist":3,
+        # "jaw":3,
+        # "left_eye_smplhf":3,
+        # "right_eye_smplhf":3,
+        # "left_index1":3,
+        # "left_index2":3,
+        # "left_index3":3,
+        # "left_middle1":3,
+        # "left_middle2":3,
+        # "left_middle3":3,
+        # "left_pinky1":3,
+        # "left_pinky2":3,
+        # "left_pinky3":3,
+        # "left_ring1":3,
+        # "left_ring2":3,
+        # "left_ring3":3,
+        # "left_thumb1":3,
+        # "left_thumb2":3,
+        # "left_thumb3":3,
+        # "right_index1":3,
+        # "right_index2":3,
+        # "right_index3":3,
+        # "right_middle1":3,
+        # "right_middle2":3,
+        # "right_middle3":3,
+        # "right_pinky1":3,
+        # "right_pinky2":3,
+        # "right_pinky3":3,
+        # "right_ring1":3,
+        # "right_ring2":3,
+        # "right_ring3":3,
+        # "right_thumb1":3,
+        # "right_thumb2":3,
+        # "right_thumb3":3,
+    },
+
+    "beat_smplx_face": {
+        # "pelvis":3,
+        # "left_hip":3,
+        # "right_hip":3,
+        # # "spine1":3,
+        # "left_knee":3,
+        # "right_knee":3,
+        # # "spine2":3,
+        # "left_ankle":3,
+        # "right_ankle":3,
+        # # "spine3":3,
+        # "left_foot":3,
+        # "right_foot":3,
+        # "neck":3,
+        # "left_collar":3,
+        # "right_collar":3,
+        # "head":3,
+        # "left_shoulder":3,
+        # "right_shoulder":3,
+        # "left_elbow":3,
+        # "right_elbow":3,
+        # "left_wrist":3,
+        # "right_wrist":3,
+        "jaw":3,
+        # "left_eye_smplhf":3,
+        # "right_eye_smplhf":3,
+        # "left_index1":3,
+        # "left_index2":3,
+        # "left_index3":3,
+        # "left_middle1":3,
+        # "left_middle2":3,
+        # "left_middle3":3,
+        # "left_pinky1":3,
+        # "left_pinky2":3,
+        # "left_pinky3":3,
+        # "left_ring1":3,
+        # "left_ring2":3,
+        # "left_ring3":3,
+        # "left_thumb1":3,
+        # "left_thumb2":3,
+        # "left_thumb3":3,
+        # "right_index1":3,
+        # "right_index2":3,
+        # "right_index3":3,
+        # "right_middle1":3,
+        # "right_middle2":3,
+        # "right_middle3":3,
+        # "right_pinky1":3,
+        # "right_pinky2":3,
+        # "right_pinky3":3,
+        # "right_ring1":3,
+        # "right_ring2":3,
+        # "right_ring3":3,
+        # "right_thumb1":3,
+        # "right_thumb2":3,
+        # "right_thumb3":3,
+    },
+    
+        "beat_joints": {
+        'Hips':         [6,6],
+        'Spine':        [3,9],
+        'Spine1':       [3,12],
+        'Spine2':       [3,15],
+        'Spine3':       [3,18],
+        'Neck':         [3,21],
+        'Neck1':        [3,24],
+        'Head':         [3,27],
+        'HeadEnd':      [3,30],
+
+        'RShoulder':    [3,33], 
+        'RArm':         [3,36],
+        'RArm1':        [3,39],
+        'RHand':        [3,42],    
+        'RHandM1':      [3,45],
+        'RHandM2':      [3,48],
+        'RHandM3':      [3,51],
+        'RHandM4':      [3,54],
+
+        'RHandR':       [3,57],
+        'RHandR1':      [3,60],
+        'RHandR2':      [3,63],
+        'RHandR3':      [3,66],
+        'RHandR4':      [3,69],
+
+        'RHandP':       [3,72],
+        'RHandP1':      [3,75],
+        'RHandP2':      [3,78],
+        'RHandP3':      [3,81],
+        'RHandP4':      [3,84],
+
+        'RHandI':       [3,87],
+        'RHandI1':      [3,90],
+        'RHandI2':      [3,93],
+        'RHandI3':      [3,96],
+        'RHandI4':      [3,99],
+
+        'RHandT1':      [3,102],
+        'RHandT2':      [3,105],
+        'RHandT3':      [3,108],
+        'RHandT4':      [3,111],
+
+        'LShoulder':    [3,114], 
+        'LArm':         [3,117],
+        'LArm1':        [3,120],
+        'LHand':        [3,123],    
+        'LHandM1':      [3,126],
+        'LHandM2':      [3,129],
+        'LHandM3':      [3,132],
+        'LHandM4':      [3,135],
+
+        'LHandR':       [3,138],
+        'LHandR1':      [3,141],
+        'LHandR2':      [3,144],
+        'LHandR3':      [3,147],
+        'LHandR4':      [3,150],
+
+        'LHandP':       [3,153],
+        'LHandP1':      [3,156],
+        'LHandP2':      [3,159],
+        'LHandP3':      [3,162],
+        'LHandP4':      [3,165],
+
+        'LHandI':       [3,168],
+        'LHandI1':      [3,171],
+        'LHandI2':      [3,174],
+        'LHandI3':      [3,177],
+        'LHandI4':      [3,180],
+
+        'LHandT1':      [3,183],
+        'LHandT2':      [3,186],
+        'LHandT3':      [3,189],
+        'LHandT4':      [3,192],
+
+        'RUpLeg':       [3,195],
+        'RLeg':         [3,198],
+        'RFoot':        [3,201],
+        'RFootF':       [3,204],
+        'RToeBase':     [3,207],
+        'RToeBaseEnd':  [3,210],
+
+        'LUpLeg':       [3,213],
+        'LLeg':         [3,216],
+        'LFoot':        [3,219],
+        'LFootF':       [3,222],
+        'LToeBase':     [3,225],
+        'LToeBaseEnd':  [3,228],},
+    
+    "beat_full":{
+        'Hips': 3,
+        'Spine':       3 ,
+        'Spine1':       3 ,
+        'Spine2':       3 ,
+        'Spine3':       3 ,
+        'Neck':        3 ,
+        'Neck1':       3 ,
+        'Head' :       3,
+        'HeadEnd' :       3,
+        'RShoulder':   3 , 
+        'RArm':        3 ,
+        'RArm1':       3 ,
+        'RHand':       3 ,    
+        'RHandM1':     3 ,
+        'RHandM2':     3 ,
+        'RHandM3':     3 ,
+        'RHandM4':     3 ,
+        'RHandR':      3 ,
+        'RHandR1':     3 ,
+        'RHandR2':     3 ,
+        'RHandR3':     3 ,
+        'RHandR4':     3 ,
+        'RHandP':      3 ,
+        'RHandP1':     3 ,
+        'RHandP2':     3 ,
+        'RHandP3':     3 ,
+        'RHandP4':     3 ,
+        'RHandI':      3 ,
+        'RHandI1':     3 ,
+        'RHandI2':     3 ,
+        'RHandI3':     3 ,
+        'RHandI4':     3 ,
+        'RHandT1':     3 ,
+        'RHandT2':     3 ,
+        'RHandT3':     3 ,
+        'RHandT4':     3 ,
+        'LShoulder':   3 , 
+        'LArm':        3 ,
+        'LArm1':       3 ,
+        'LHand':       3 ,    
+        'LHandM1':     3 ,
+        'LHandM2':     3 ,
+        'LHandM3':     3 ,
+        'LHandM4':     3 ,
+        'LHandR':      3 ,
+        'LHandR1':     3 ,
+        'LHandR2':     3 ,
+        'LHandR3':     3 ,
+        'LHandR4':     3 ,
+        'LHandP':      3 ,
+        'LHandP1':     3 ,
+        'LHandP2':     3 ,
+        'LHandP3':     3 ,
+        'LHandP4':     3 ,
+        'LHandI':      3 ,
+        'LHandI1':     3 ,
+        'LHandI2':     3 ,
+        'LHandI3':     3 ,
+        'LHandI4':     3 ,
+        'LHandT1':     3 ,
+        'LHandT2':     3 ,
+        'LHandT3':     3 ,
+        'LHandT4':     3 ,
+        'RUpLeg':      3,
+        'RLeg':        3,
+        'RFoot':       3,
+        'RFootF':       3,
+        'RToeBase':     3,
+        'RToeBaseEnd':  3,
+        'LUpLeg':       3,
+        'LLeg':         3,
+        'LFoot':        3,
+        'LFootF':       3,
+        'LToeBase':     3,
+        'LToeBaseEnd':  3,
+    },
+    
+    "japanese_joints":{
+        'Hips':         [6,6],
+        'Spine':        [6,12],
+        'Spine1':       [6,18],
+        'Spine2':       [6,24],
+        'Spine3':       [6,30],
+        'Neck':         [6,36],
+        'Neck1':        [6,42],
+        'Head':         [6,48],
+        'RShoulder':    [6,54], 
+        'RArm':         [6,60],
+        'RArm1':        [6,66],
+        'RHand':        [6,72],    
+        'RHandM1':      [6,78],
+        'RHandM2':      [6,84],
+        'RHandM3':      [6,90],
+        'RHandR':       [6,96],
+        'RHandR1':      [6,102],
+        'RHandR2':      [6,108],
+        'RHandR3':      [6,114],
+        'RHandP':       [6,120],
+        'RHandP1':      [6,126],
+        'RHandP2':      [6,132],
+        'RHandP3':      [6,138],
+        'RHandI':       [6,144],
+        'RHandI1':      [6,150],
+        'RHandI2':      [6,156],
+        'RHandI3':      [6,162],
+        'RHandT1':      [6,168],
+        'RHandT2':      [6,174],
+        'RHandT3':      [6,180],
+        'LShoulder':    [6,186], 
+        'LArm':         [6,192],
+        'LArm1':        [6,198],
+        'LHand':        [6,204],    
+        'LHandM1':      [6,210],
+        'LHandM2':      [6,216],
+        'LHandM3':      [6,222],
+        'LHandR':       [6,228],
+        'LHandR1':      [6,234],
+        'LHandR2':      [6,240],
+        'LHandR3':      [6,246],
+        'LHandP':       [6,252],
+        'LHandP1':      [6,258],
+        'LHandP2':      [6,264],
+        'LHandP3':      [6,270],
+        'LHandI':       [6,276],
+        'LHandI1':      [6,282],
+        'LHandI2':      [6,288],
+        'LHandI3':      [6,294],
+        'LHandT1':      [6,300],
+        'LHandT2':      [6,306],
+        'LHandT3':      [6,312],
+        'RUpLeg':       [6,318],
+        'RLeg':         [6,324],
+        'RFoot':        [6,330],
+        'RFootF':       [6,336],
+        'RToeBase':     [6,342],
+        'LUpLeg':       [6,348],
+        'LLeg':         [6,354],
+        'LFoot':        [6,360],
+        'LFootF':       [6,366],
+        'LToeBase':     [6,372],},
+    
+    "yostar":{
+    'Hips':         [6,6],
+    'Spine':        [3,9],
+    'Spine1':       [3,12],
+    'Bone040':       [3,15],
+    'Bone041':       [3,18],
+
+    'Bone034':         [3,21],
+    'Bone035':        [3,24],
+    'Bone036':         [3,27],
+    'Bone037':        [3,30],
+    'Bone038':         [3,33],
+    'Bone039':        [3,36],
+
+    'RibbonL1':         [3,39],
+    'RibbonL1_end':      [3,42],
+
+    'Chest':         [3,45],
+    'L_eri':      [3,48],
+    'R_eri':      [3,51],
+    'Neck':      [3,54],
+    'Head':      [3,57],
+    'Head_end':      [3,60],
+    
+    'RBackHair_1':  [3,63],
+    'RBackHair_2':  [3,66],
+    'RBackHair_3':  [3,69],
+    'RBackHair_4':  [3,72],
+    'RBackHair_end':  [3,75],
+    
+    'RFrontHair':  [3,78],
+    'CFrontHair_1':  [3,81],
+    'CFrontHair_2':  [3,84],
+    'CFrontHair_3':  [3,87],
+    'CFrontHair_emd':  [3,90],
+    
+    'LFrontHair_1':  [3,93],
+    'LFrontHair_2':  [3,96],
+    'LFrontHair_3':  [3,99],
+    
+    'LBackHair_1':  [3,102],
+    'LBackHair_2':  [3,105],
+    'LBackHair_3':  [3,108],
+    'LBackHair_4':  [3,111],
+    'LBackHair_end':  [3,114],
+    
+    'LSideHair_1':  [3,117],
+    'LSideHair_2':  [3,120],
+    'LSideHair_3':  [3,123],
+    'LSideHair_4':  [3,126],
+    'LSideHair_5':  [3,129],
+    'LSideHair_6':  [3,132],
+    'LSideHair_7':  [3,135],
+    'LSideHair_end':  [3,138],
+    
+    'CBackHair_1':  [3,141],
+    'CBackHair_2':  [3,144],
+    'CBackHair_3':  [3,147],
+    'CBackHair_4':  [3,150],
+    'CBackHair_end':  [3,153],
+    
+    'RSideHair_1':  [3,156],
+    'RSideHair_2':  [3,159],
+    'RSideHair_3':  [3,162],
+    'RSideHair_4':  [3,165],
+    
+    'RibbonR_1':  [3,168],
+    'RibbonR_2':  [3,171],
+    'RibbonR_3':  [3,174],
+    
+    'RibbonL_1':  [3,177],
+    'RibbonL_2':  [3,180],
+    'RibbonL_3':  [3,183],
+    
+    'LeftEye':  [3,186],
+    'LeftEye_end':  [3,189],
+    'RightEye':  [3,192],
+    'RightEye_end':  [3,195],
+    
+    'LeftShoulder':    [3,198], 
+    'LeftArm':         [3,201],
+    'LeftForearm':        [3,204],
+    'LeftHand':        [3,207],
+    'LeftHandThumb1':      [3,210],
+    'LeftHandThumb2':      [3,213],
+    'LeftHandThumb3':      [3,216],
+    'LeftHandThumb_end':      [3,219],
+    
+    'LeftHandIndex1':       [3,222],
+    'LeftHandIndex2':      [3,225],
+    'LeftHandIndex3':      [3,228],
+    'LeftHandIndex_end':      [3,231],
+    
+    'LeftHandMiddle1':      [3,234],
+    'LeftHandMiddle2':      [3,237],
+    'LeftHandMiddle3':      [3,240],
+    'LeftHandMiddle_end':      [3,243],
+
+    'LeftHandRing1':       [3,246],
+    'LeftHandRing2':      [3,249],
+    'LeftHandRing3':      [3,252],
+    'LeftHandRing_end':      [3,255],
+
+    'LeftHandPinky1':       [3,258],
+    'LeftHandPinky2':      [3,261],
+    'LeftHandPinky3':      [3,264],
+    'LeftHandPinky_end':      [3,267],
+
+    'RightShoulder':    [3,270], 
+    'RightArm':         [3,273],
+    'RightForearm':        [3,276],
+    'RightHand':        [3,279],
+    'RightHandThumb1':      [3,282],
+    'RightHandThumb2':      [3,285],
+    'RightHandThumb3':      [3,288],
+    'RightHandThumb_end':      [3,291],
+    
+    'RightHandIndex1':       [3,294],
+    'RightHandIndex2':      [3,297],
+    'RightHandIndex3':      [3,300],
+    'RightHandIndex_end':      [3,303],
+    
+    'RightHandMiddle1':      [3,306],
+    'RightHandMiddle2':      [3,309],
+    'RightHandMiddle3':      [3,312],
+    'RightHandMiddle_end':      [3,315],
+
+    'RightHandRing1':       [3,318],
+    'RightHandRing2':      [3,321],
+    'RightHandRing3':      [3,324],
+    'RightHandRing_end':      [3,327],
+
+    'RightHandPinky1':       [3,330],
+    'RightHandPinky2':      [3,333],
+    'RightHandPinky3':      [3,336],
+    'RightHandPinky_end':      [3,339],
+
+    'RibbonR1':  [3,342],
+    'RibbonR1_end':  [3,345],
+    'RibbonR2':  [3,348],
+    'RibbonR2_end':  [3,351],
+    'RibbonL2':  [3,354],
+    'RibbonL2_end':  [3,357],
+    
+    'LeftUpLeg':       [3,360],
+    'LeftLeg':         [3,363],
+    'LeftFoot':        [3,366],
+    'LeftToe':       [3,369],
+    'LeftToe_end':     [3,372],
+   
+    'RightUpLeg':       [3,375],
+    'RightLEg':         [3,378],
+    'RightFoot':        [3,381],
+    'RightToe':       [3,384],
+    'RightToe_end':     [3,387],
+    
+    'bone_skirtF00': [3, 390],
+    'bone_skirtF01': [3, 393],
+    'bone_skirtF02': [3, 396],
+    'bone_skirtF03': [3, 399],
+    'Bone020': [3, 402],
+    'Bone026': [3, 405],
+    
+    'bone_skirtF_R_00': [3, 408],
+    'bone_skirtF_R_01': [3, 411],
+    'bone_skirtF_R_02': [3, 414],
+    'bone_skirtF_R_03': [3, 417],
+    'Bone019': [3, 420],
+    'Bone028': [3, 423],
+    
+    'bone_skirtR00': [3, 426],
+    'bone_skirtR01': [3, 429],
+    'bone_skirtR02': [3, 432],
+    'bone_skirtR03': [3, 435],
+    'Bone018': [3, 438],
+    'Bone029': [3, 441],
+    
+    'bone_skirtF_L_00': [3, 444],
+    'bone_skirtF_L_01': [3, 447],
+    'bone_skirtF_L_02': [3, 450],
+    'bone_skirtF_L_03': [3, 453],
+    'Bone021': [3, 456],
+    'Bone027': [3, 459],
+    
+    'bone_skirtL00': [3, 462],
+    'bone_skirtL01': [3, 465],
+    'bone_skirtL02': [3, 468],
+    'bone_skirtL03': [3, 471],
+    'Bone022': [3, 474],
+    'Bone033': [3, 477],
+    
+    'bone_skirtB_L_00': [3, 480],
+    'bone_skirtB_L_01': [3, 483],
+    'bone_skirtB_L_02': [3, 486],
+    'bone_skirtB_L_03': [3, 489],
+    'Bone023': [3, 492],
+    'Bone032': [3, 495],
+    
+    'bone_skirtB00': [3, 498],
+    'bone_skirtB01': [3, 501],
+    'bone_skirtB02': [3, 504],
+    'bone_skirtB03': [3, 507],
+    'Bone024': [3, 510],
+    'Bone031': [3, 513],
+    
+    'bone_skirtB_R_00': [3, 516],
+    'bone_skirtB_R_01': [3, 519],
+    'bone_skirtB_R_02': [3, 521],
+    'bone_skirtB_R_03': [3, 524],
+    'Bone025': [3, 527],
+    'Bone030': [3, 530],
+        },
+        
+    "yostar_fullbody_213":{
+    'Hips':       3 ,
+    'Spine':       3 ,
+    'Spine1':        3 ,
+    'Chest':       3 ,
+    'L_eri':       3 ,
+    'R_eri':       3 ,
+    'Neck':   3 , 
+    'Head':        3 ,
+    'Head_end':       3 ,
+    
+    'LeftEye':  3,
+    'LeftEye_end':  3,
+    'RightEye':  3,
+    'RightEye_end':  3,
+    
+    'LeftShoulder':    3, 
+    'LeftArm':       3, 
+    'LeftForearm':     3, 
+    'LeftHand':      3, 
+    'LeftHandThumb1':     3, 
+    'LeftHandThumb2':    3, 
+    'LeftHandThumb3':     3, 
+    'LeftHandThumb_end':    3, 
+    
+    'LeftHandIndex1':    3, 
+    'LeftHandIndex2':    3, 
+    'LeftHandIndex3':  3, 
+    'LeftHandIndex_end':    3, 
+    
+    'LeftHandMiddle1':    3, 
+    'LeftHandMiddle2':   3, 
+    'LeftHandMiddle3':    3, 
+    'LeftHandMiddle_end':    3, 
+
+    'LeftHandRing1':  3, 
+    'LeftHandRing2':     3, 
+    'LeftHandRing3':     3, 
+    'LeftHandRing_end':    3, 
+
+    'LeftHandPinky1':      3, 
+    'LeftHandPinky2':     3, 
+    'LeftHandPinky3':     3, 
+    'LeftHandPinky_end':3, 
+
+    'RightShoulder':   3, 
+    'RightArm':        3, 
+    'RightForearm':     3, 
+    'RightHand':      3, 
+    'RightHandThumb1':    3, 
+    'RightHandThumb2':     3, 
+    'RightHandThumb3':     3, 
+    'RightHandThumb_end':     3, 
+    
+    'RightHandIndex1':      3, 
+    'RightHandIndex2':    3, 
+    'RightHandIndex3':     3, 
+    'RightHandIndex_end':    3, 
+    
+    'RightHandMiddle1':    3, 
+    'RightHandMiddle2':   3, 
+    'RightHandMiddle3':      3, 
+    'RightHandMiddle_end':    3, 
+
+    'RightHandRing1':     3, 
+    'RightHandRing2':    3, 
+    'RightHandRing3':      3, 
+    'RightHandRing_end':    3, 
+
+    'RightHandPinky1':     3, 
+    'RightHandPinky2':   3, 
+    'RightHandPinky3':     3, 
+    'RightHandPinky_end':    3, 
+    
+    'LeftUpLeg':       3,
+    'LeftLeg':         3,
+    'LeftFoot':       3,
+    'LeftToe':     3,
+    'LeftToe_end':    3,
+   
+    'RightUpLeg':     3,
+    'RightLEg':      3,
+    'RightFoot':       3,
+    'RightToe':      3,
+    'RightToe_end':    3,
+        },
+    "yostar_mainbody_48": {
+    #'Hips':       3 ,
+    'Spine':       3 ,
+    'Spine1':        3 ,
+    'Chest':       3 ,
+    'L_eri':       3 ,
+    'R_eri':       3 ,
+    'Neck':   3 , 
+    'Head':        3 ,
+    'Head_end':       3 ,
+    
+    'LeftShoulder':    3, 
+    'LeftArm':       3, 
+    'LeftForearm':     3, 
+    'LeftHand':      3, 
+
+    'RightShoulder':   3, 
+    'RightArm':        3, 
+    'RightForearm':     3, 
+    'RightHand':      3, 
+    },
+    "yostar_mainbody_69": {
+    'Hips':       3 ,
+    'Spine':       3 ,
+    'Spine1':        3 ,
+    'Chest':       3 ,
+    'L_eri':       3 ,
+    'R_eri':       3 ,
+    'Neck':   3 , 
+    'Head':        3 ,
+    'Head_end':       3 ,
+    
+    'LeftShoulder':    3, 
+    'LeftArm':       3, 
+    'LeftForearm':     3, 
+    'LeftHand':      3, 
+
+    'RightShoulder':   3, 
+    'RightArm':        3, 
+    'RightForearm':     3, 
+    'RightHand':      3, 
+    
+    'LeftUpLeg':       3,
+    'LeftLeg':         3,
+    'LeftFoot':       3,
+   
+    'RightUpLeg':     3,
+    'RightLEg':      3,
+    'RightFoot':       3,
+    },
+    
+    "yostar_upbody_168": {
+    #'Hips':       3 ,
+    'Spine':       3 ,
+    'Spine1':        3 ,
+    'Chest':       3 ,
+    'L_eri':       3 ,
+    'R_eri':       3 ,
+    'Neck':   3 , 
+    'Head':        3 ,
+    'Head_end':       3 ,
+    
+    'LeftShoulder':    3, 
+    'LeftArm':       3, 
+    'LeftForearm':     3, 
+    'LeftHand':      3, 
+    'LeftHandThumb1':     3, 
+    'LeftHandThumb2':    3, 
+    'LeftHandThumb3':     3, 
+    'LeftHandThumb_end':    3, 
+    
+    'LeftHandIndex1':    3, 
+    'LeftHandIndex2':    3, 
+    'LeftHandIndex3':  3, 
+    'LeftHandIndex_end':    3, 
+    
+    'LeftHandMiddle1':    3, 
+    'LeftHandMiddle2':   3, 
+    'LeftHandMiddle3':    3, 
+    'LeftHandMiddle_end':    3, 
+
+    'LeftHandRing1':  3, 
+    'LeftHandRing2':     3, 
+    'LeftHandRing3':     3, 
+    'LeftHandRing_end':    3, 
+
+    'LeftHandPinky1':      3, 
+    'LeftHandPinky2':     3, 
+    'LeftHandPinky3':     3, 
+    'LeftHandPinky_end':3, 
+        
+    'RightShoulder':   3, 
+    'RightArm':        3, 
+    'RightForearm':     3, 
+    'RightHand':      3, 
+    'RightHandThumb1':    3, 
+    'RightHandThumb2':     3, 
+    'RightHandThumb3':     3, 
+    'RightHandThumb_end':     3, 
+    
+    'RightHandIndex1':      3, 
+    'RightHandIndex2':    3, 
+    'RightHandIndex3':     3, 
+    'RightHandIndex_end':    3, 
+    
+    'RightHandMiddle1':    3, 
+    'RightHandMiddle2':   3, 
+    'RightHandMiddle3':      3, 
+    'RightHandMiddle_end':    3, 
+
+    'RightHandRing1':     3, 
+    'RightHandRing2':    3, 
+    'RightHandRing3':      3, 
+    'RightHandRing_end':    3, 
+
+    'RightHandPinky1':     3, 
+    'RightHandPinky2':   3, 
+    'RightHandPinky3':     3, 
+    'RightHandPinky_end':    3, 
+    },
+    "spine_neck_141":{
+        'Spine':       3 ,
+        'Neck':        3 ,
+        'Neck1':       3 ,
+        'RShoulder':   3 , 
+        'RArm':        3 ,
+        'RArm1':       3 ,
+        'RHand':       3 ,    
+        'RHandM1':     3 ,
+        'RHandM2':     3 ,
+        'RHandM3':     3 ,
+        'RHandR':      3 ,
+        'RHandR1':     3 ,
+        'RHandR2':     3 ,
+        'RHandR3':     3 ,
+        'RHandP':      3 ,
+        'RHandP1':     3 ,
+        'RHandP2':     3 ,
+        'RHandP3':     3 ,
+        'RHandI':      3 ,
+        'RHandI1':     3 ,
+        'RHandI2':     3 ,
+        'RHandI3':     3 ,
+        'RHandT1':     3 ,
+        'RHandT2':     3 ,
+        'RHandT3':     3 ,
+        'LShoulder':   3 , 
+        'LArm':        3 ,
+        'LArm1':       3 ,
+        'LHand':       3 ,    
+        'LHandM1':     3 ,
+        'LHandM2':     3 ,
+        'LHandM3':     3 ,
+        'LHandR':      3 ,
+        'LHandR1':     3 ,
+        'LHandR2':     3 ,
+        'LHandR3':     3 ,
+        'LHandP':      3 ,
+        'LHandP1':     3 ,
+        'LHandP2':     3 ,
+        'LHandP3':     3 ,
+        'LHandI':      3 ,
+        'LHandI1':     3 ,
+        'LHandI2':     3 ,
+        'LHandI3':     3 ,
+        'LHandT1':     3 ,
+        'LHandT2':     3 ,
+        'LHandT3':     3 ,},
+}
+
+
+class FIDCalculator(object):
+    '''
+    todo
+    '''
+    def __init__(self):
+        self.gt_rot = None # pandas dataframe for n frames * joints * 6
+        self.gt_pos = None # n frames * (joints + 13) * 3
+        self.op_rot = None # pandas dataframe for n frames * joints * 6
+        self.op_pos = None # n frames * (joints + 13) * 3
+        
+
+    def load(self, path, load_type, save_pos=False):
+        '''
+        select gt or op for load_type
+        '''
+        parser = BVHParser()
+        parsed_data = parser.parse(path)
+        if load_type == 'gt':
+            self.gt_rot = parsed_data.values
+        elif load_type == 'op':
+            self.op_rot = parsed_data.values
+        else: print('error, select gt or op for load_type')
+
+        if save_pos:
+            mp = MocapParameterizer('position')
+            positions = mp.fit_transform([parsed_data])
+            if load_type == 'gt':
+                self.gt_pos = positions[0].values
+            elif load_type == 'op':
+                self.op_pos = positions[0].values
+            else: print('error, select gt or op for load_type')
+
+
+    def _joint_selector(self, selected_joints, ori_data):
+        selected_data = pd.DataFrame(columns=[])
+
+        for joint_name in selected_joints:
+            selected_data[joint_name] = ori_data[joint_name]
+        return selected_data.to_numpy()
+    
+    
+    def cal_vol(self, dtype):
+        if dtype == 'pos':
+            gt = self.gt_pos
+            op = self.op_pos
+        else:
+            gt = self.gt_rot
+            op = self.op_rot
+        
+        gt_v = gt.to_numpy()[1:, :] - gt.to_numpy()[0:-1, :]
+        op_v = op.to_numpy()[1:, :] - op.to_numpy()[0:-1, :]
+        if dtype == 'pos':
+            self.gt_vol_pos = pd.DataFrame(gt_v, columns = gt.columns.tolist())
+            self.op_vol_pos = pd.DataFrame(op_v, columns = gt.columns.tolist())
+        else:
+            self.gt_vol_rot = pd.DataFrame(gt_v, columns = gt.columns.tolist())
+            self.op_vol_rot = pd.DataFrame(op_v, columns = gt.columns.tolist())
+
+
+    @staticmethod
+    def frechet_distance(samples_A, samples_B):
+        A_mu = np.mean(samples_A, axis=0)
+        A_sigma = np.cov(samples_A, rowvar=False)
+        B_mu = np.mean(samples_B, axis=0)
+        B_sigma = np.cov(samples_B, rowvar=False)
+        try:
+            frechet_dist = FIDCalculator.calculate_frechet_distance(A_mu, A_sigma, B_mu, B_sigma)
+        except ValueError:
+            frechet_dist = 1e+10
+        return frechet_dist
+
+
+    @staticmethod
+    def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
+        """ from https://github.com/mseitzer/pytorch-fid/blob/master/fid_score.py """
+        """Numpy implementation of the Frechet Distance.
+        The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
+        and X_2 ~ N(mu_2, C_2) is
+                d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
+        Stable version by Dougal J. Sutherland.
+        Params:
+        -- mu1   : Numpy array containing the activations of a layer of the
+                    inception net (like returned by the function 'get_predictions')
+                    for generated samples.
+        -- mu2   : The sample mean over activations, precalculated on an
+                    representative data set.
+        -- sigma1: The covariance matrix over activations for generated samples.
+        -- sigma2: The covariance matrix over activations, precalculated on an
+                    representative data set.
+        Returns:
+        --   : The Frechet Distance.
+        """
+
+        mu1 = np.atleast_1d(mu1)
+        mu2 = np.atleast_1d(mu2)
+        #print(mu1[0], mu2[0])
+        sigma1 = np.atleast_2d(sigma1)
+        sigma2 = np.atleast_2d(sigma2)
+        #print(sigma1[0], sigma2[0])
+        assert mu1.shape == mu2.shape, \
+            'Training and test mean vectors have different lengths'
+        assert sigma1.shape == sigma2.shape, \
+            'Training and test covariances have different dimensions'
+
+        diff = mu1 - mu2
+
+        # Product might be almost singular
+        covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+        #print(diff, covmean[0])
+        if not np.isfinite(covmean).all():
+            msg = ('fid calculation produces singular product; '
+                    'adding %s to diagonal of cov estimates') % eps
+            print(msg)
+            offset = np.eye(sigma1.shape[0]) * eps
+            covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+        # Numerical error might give slight imaginary component
+        if np.iscomplexobj(covmean):
+            if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+                m = np.max(np.abs(covmean.imag))
+                raise ValueError('Imaginary component {}'.format(m))
+            covmean = covmean.real
+
+        tr_covmean = np.trace(covmean)
+
+        return (diff.dot(diff) + np.trace(sigma1) +
+                np.trace(sigma2) - 2 * tr_covmean)
+
+    
+    def calculate_fid(self, cal_type, joint_type, high_level_opt):
+        
+        if cal_type == 'pos':
+            if self.gt_pos.shape != self.op_pos.shape:
+                min_val = min(self.gt_pos.shape[0],self.op_pos.shape[0])
+                gt = self.gt_pos[:min_val]
+                op = self.op_pos[:min_val]
+            else:
+                gt = self.gt_pos
+                op = self.op_pos
+            full_body = gt.columns.tolist()
+        elif cal_type == 'rot':
+            if self.gt_rot.shape != self.op_rot.shape:
+                min_val = min(self.gt_rot.shape[0],self.op_rot.shape[0])
+                gt = self.gt_rot[:min_val]
+                op = self.op_rot[:min_val]
+            else:
+                gt = self.gt_rot
+                op = self.op_rot
+            full_body_with_offset = gt.columns.tolist()
+            full_body = [o for o in full_body_with_offset if ('position' not in o)]
+        elif cal_type == 'pos_vol':
+            assert self.gt_vol_pos.shape == self.op_vol_pos.shape
+            gt = self.gt_vol_pos
+            op = self.op_vol_pos
+            full_body_with_offset = gt.columns.tolist()
+            full_body = gt.columns.tolist()
+        elif cal_type == 'rot_vol':
+            assert self.gt_vol_rot.shape == self.op_vol_rot.shape
+            gt = self.gt_vol_rot
+            op = self.op_vol_rot
+            full_body_with_offset = gt.columns.tolist()
+            full_body = [o for o in full_body_with_offset if ('position' not in o)]       
+        #print(f'full_body contains {len(full_body)//3} joints')
+
+        if joint_type == 'full_upper_body':
+            selected_body = [o for o in full_body if ('Leg' not in o) and ('Foot' not in o) and ('Toe' not in o)] 
+        elif joint_type == 'upper_body':
+            selected_body = [o for o in full_body if ('Hand' not in o) and ('Leg' not in o) and ('Foot' not in o) and ('Toe' not in o)]
+        elif joint_type == 'fingers':
+            selected_body = [o for o in full_body if ('Hand' in o)]
+        elif joint_type == 'indivdual':
+            pass
+        else: print('error, plz select correct joint type')
+        #print(f'calculate fid for {len(selected_body)//3} joints')
+
+        gt = self._joint_selector(selected_body, gt)
+        op = self._joint_selector(selected_body, op)
+
+        if high_level_opt == 'fid':
+            fid = FIDCalculator.frechet_distance(gt, op)
+            return fid
+        elif high_level_opt == 'var':
+            var_gt = gt.var()
+            var_op = op.var()
+            return var_gt, var_op
+        elif high_level_opt == 'mean':
+            mean_gt = gt.mean()
+            mean_op = op.mean()
+            return mean_gt, mean_op
+        else: return 0
+             
+
+def result2target_vis(pose_version, res_bvhlist, save_path, demo_name, verbose=True):
+    if "trinity" in pose_version:
+        ori_list = joints_list[pose_version[6:-4]] 
+        target_list = joints_list[pose_version[6:]] 
+        file_content_length = 336 
+    elif "beat" in pose_version or "spine_neck_141" in pose_version:
+        ori_list = joints_list["beat_joints"]
+        target_list = joints_list["spine_neck_141"]
+        file_content_length = 431
+    elif "yostar" in pose_version:
+        ori_list = joints_list["yostar"]
+        target_list = joints_list[pose_version]
+        file_content_length = 1056
+    else:
+        ori_list = joints_list["japanese_joints"]
+        target_list = joints_list[pose_version]
+        file_content_length = 366
+    
+    bvh_files_dirs = sorted(glob.glob(f'{res_bvhlist}*.bvh'), key=str)
+    #test_seq_list = os.list_dir(demo_name).sort()
+    
+    counter = 0
+    if not os.path.exists(save_path):
+        os.makedirs(save_path)
+    for i, bvh_file_dir in enumerate(bvh_files_dirs):
+        short_name = bvh_file_dir.split("/")[-1][11:]
+        #print(short_name)
+        wirte_file =  open(os.path.join(save_path, f'res_{short_name}'),'w+')
+        with open(f"{demo_name}{short_name}",'r') as pose_data_pre:
+            pose_data_pre_file = pose_data_pre.readlines()
+            for j, line in enumerate(pose_data_pre_file[0:file_content_length]):
+                    wirte_file.write(line)
+            offset_data = pose_data_pre_file[file_content_length]
+            offset_data = np.fromstring(offset_data, dtype=float, sep=' ')
+        wirte_file.close()
+
+        wirte_file = open(os.path.join(save_path, f'res_{short_name}'),'r')
+        ori_lines = wirte_file.readlines()
+        with open(bvh_file_dir, 'r') as pose_data:
+            pose_data_file = pose_data.readlines()
+        ori_lines[file_content_length-2] = 'Frames: ' + str(len(pose_data_file)-1) + '\n'
+        wirte_file.close() 
+
+        wirte_file = open(os.path.join(save_path, f'res_{short_name}'),'w+')
+        wirte_file.writelines(i for i in ori_lines[:file_content_length])    
+        wirte_file.close() 
+
+        with open(os.path.join(save_path, f'res_{short_name}'),'a+') as wirte_file: 
+            with open(bvh_file_dir, 'r') as pose_data:
+                data_each_file = []
+                pose_data_file = pose_data.readlines()
+                for j, line in enumerate(pose_data_file):
+                    if not j:
+                        pass
+                    else:          
+                        data = np.fromstring(line, dtype=float, sep=' ')
+                        data_rotation = offset_data.copy()   
+                        for iii, (k, v) in enumerate(target_list.items()): # here is 147 rotations by 3
+                            #print(data_rotation[ori_list[k][1]-v:ori_list[k][1]], data[iii*3:iii*3+3])
+                            data_rotation[ori_list[k][1]-v:ori_list[k][1]] = data[iii*3:iii*3+3]
+                        data_each_file.append(data_rotation)
+        
+            for line_data in data_each_file:
+                line_data = np.array2string(line_data, max_line_width=np.inf, precision=6, suppress_small=False, separator=' ')
+                wirte_file.write(line_data[1:-2]+'\n')
+
+        counter += 1
+        if verbose:
+            logger.info('data_shape:', data_rotation.shape, 'process:', counter, '/', len(bvh_files_dirs))

dataloaders/mix_sep.py

@@ -0,0 +1,637 @@
+import os
+import pickle
+import math
+import shutil
+import numpy as np
+import lmdb as lmdb
+import textgrid as tg
+import pandas as pd
+import torch
+import glob
+import json
+from termcolor import colored
+from loguru import logger
+from collections import defaultdict
+from torch.utils.data import Dataset
+import torch.distributed as dist
+#import pyarrow
+import pickle
+import librosa
+import smplx
+import glob
+
+from .build_vocab import Vocab
+from .utils.audio_features import Wav2Vec2Model
+from .data_tools import joints_list
+from .utils import rotation_conversions as rc
+from .utils import other_tools
+
+# ACCAD 120
+# BioMotionLab_NTroje 120
+# CMU 很复杂
+# EKUT 100
+# Eyes_Japan_Dataset 很复杂
+# HumanEva 很复杂
+# KIT 100
+# MPI_HDM05 120
+# MPI_Limits 120
+# MPI_mosh 很复杂
+# SFU 120
+# SSM_synced 很复杂
+# TCD_handMocap 很复杂
+# TotalCapture 60
+# Transitions_mocap 120
+
+all_sequences = [
+    'ACCAD',
+    'BioMotionLab_NTroje',
+    'CMU',
+    'EKUT',
+    'Eyes_Japan_Dataset',
+    'HumanEva',
+    'KIT',
+    'MPI_HDM05',
+    'MPI_Limits',
+    'MPI_mosh',
+    'SFU',
+    'SSM_synced',
+    'TCD_handMocap',
+    'TotalCapture',
+    'Transitions_mocap',
+]
+amass_test_split = ['Transitions_mocap', 'SSM_synced']
+amass_vald_split = ['HumanEva', 'MPI_HDM05', 'SFU', 'MPI_mosh']
+amass_train_split = ['BioMotionLab_NTroje', 'Eyes_Japan_Dataset', 'TotalCapture', 'KIT', 'ACCAD', 'CMU', 'MPI_Limits',
+                     'TCD_handMocap', 'EKUT']
+
+# 上面这些spilt方式是MOTION CLIP的，但是由于motionx中的framerate处理有问题，我先暂且只挑部分数据集进行训练
+# 这些都是120fps的
+# amass_test_split = ['SFU']
+# amass_vald_split = ['MPI_Limits']
+# amass_train_split = ['BioMotionLab_NTroje', 'MPI_HDM05', 'ACCAD','Transitions_mocap']
+
+
+amass_splits = {
+    'test': amass_test_split,
+    'val': amass_vald_split,
+    'train': amass_train_split
+}
+# assert len(amass_splits['train'] + amass_splits['test'] + amass_splits['vald']) == len(all_sequences) == 15
+
+class CustomDataset(Dataset):
+    def __init__(self, args, loader_type, augmentation=None, kwargs=None, build_cache=True):
+        self.args = args
+        self.loader_type = loader_type
+
+        self.rank = 0
+        self.ori_stride = self.args.stride
+        self.ori_length = self.args.pose_length
+        self.alignment = [0,0] # for trinity
+        
+        self.ori_joint_list = joints_list[self.args.ori_joints]
+        self.tar_joint_list = joints_list[self.args.tar_joints]
+        if 'smplx' in self.args.pose_rep:
+            self.joint_mask = np.zeros(len(list(self.ori_joint_list.keys()))*3)
+            self.joints = len(list(self.tar_joint_list.keys()))  
+            for joint_name in self.tar_joint_list:
+                self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        else:
+            self.joints = len(list(self.ori_joint_list.keys()))+1
+            self.joint_mask = np.zeros(self.joints*3)
+            for joint_name in self.tar_joint_list:
+                if joint_name == "Hips":
+                    self.joint_mask[3:6] = 1
+                else:
+                    self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        # select trainable joints
+        
+        split_rule = pd.read_csv(args.data_path+"train_test_split.csv")
+        self.selected_file = split_rule.loc[(split_rule['type'] == loader_type) & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+        if args.additional_data and loader_type == 'train':
+            split_b = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            #self.selected_file = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            self.selected_file = pd.concat([self.selected_file, split_b])
+        if self.selected_file.empty:
+            logger.warning(f"{loader_type} is empty for speaker {self.args.training_speakers}, use train set 0-8 instead")
+            self.selected_file = split_rule.loc[(split_rule['type'] == 'train') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))]
+            self.selected_file = self.selected_file.iloc[0:8]
+        self.data_dir = args.data_path
+        self.use_amass = args.use_amass
+        self.beatx_during_time = 0
+        self.amass_during_time = 0
+        
+        if loader_type == "test": 
+            self.args.multi_length_training = [1.0]
+        self.max_length = int(args.pose_length * self.args.multi_length_training[-1])
+        self.max_audio_pre_len = math.floor(args.pose_length / args.pose_fps * self.args.audio_sr)
+        if self.max_audio_pre_len > self.args.test_length*self.args.audio_sr: 
+            self.max_audio_pre_len = self.args.test_length*self.args.audio_sr
+        preloaded_dir = self.args.root_path + self.args.cache_path + loader_type + f"/{args.pose_rep}_cache"      
+
+        if self.args.beat_align:
+            if not os.path.exists(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy"):
+                self.calculate_mean_velocity(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy")
+            self.avg_vel = np.load(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy")
+            
+        if build_cache and self.rank == 0:
+            self.build_cache(preloaded_dir)
+        self.lmdb_env = lmdb.open(preloaded_dir, readonly=True, lock=False)
+        with self.lmdb_env.begin() as txn:
+            self.n_samples = txn.stat()["entries"] 
+            
+        self.norm = True
+        self.mean = np.load('./mean_std/beatx_2_330_mean.npy')
+        self.std = np.load('./mean_std/beatx_2_330_std.npy')
+        
+        self.trans_mean = np.load('./mean_std/beatx_2_trans_mean.npy')
+        self.trans_std = np.load('./mean_std/beatx_2_trans_std.npy')
+
+    def load_amass(self,data):
+        ## 这个是用来
+        # 修改amass数据里面的朝向，原本在blender里面是Z轴向上，目标是Y轴向上，当时面向目前没改
+        
+        data_dict = {key: data[key] for key in data}
+        frames = data_dict['poses'].shape[0]
+        b = data_dict['poses'][...,:3]
+        b = rc.axis_angle_to_matrix(torch.from_numpy(b))
+        rot_matrix = np.array([[1.0, 0.0, 0.0], [0.0 , 0.0, 1.0], [0.0, -1.0, 0.0]])
+        c = np.einsum('ij,kjl->kil',rot_matrix,b)
+        c = rc.matrix_to_axis_angle(torch.from_numpy(c))
+        data_dict['poses'][...,:3] = c
+        
+        trans_matrix1 = np.array([[1.0, 0.0, 0.0], [0.0 , 0.0, -1.0], [0.0, 1.0, 0.0]])
+        data_dict['trans'] = np.einsum("bi,ij->bj",data_dict['trans'],trans_matrix1)
+        
+        betas300 = np.zeros(300)
+        betas300[:16] = data_dict['betas']
+        data_dict['betas'] = betas300
+        data_dict["expressions"] = np.zeros((frames,100))
+        
+        return data_dict
+
+
+    def calculate_mean_velocity(self, save_path):
+        self.smplx = smplx.create(
+            self.args.data_path_1+"smplx_models/", 
+            model_type='smplx',
+            gender='NEUTRAL_2020', 
+            use_face_contour=False,
+            num_betas=300,
+            num_expression_coeffs=100, 
+            ext='npz',
+            use_pca=False,
+        ).cuda().eval()
+        dir_p = self.data_dir + self.args.pose_rep + "/"
+        all_list = []
+        from tqdm import tqdm
+        for tar in tqdm(os.listdir(dir_p)):
+            if tar.endswith(".npz"):
+                m_data = np.load(dir_p+tar, allow_pickle=True)
+                betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"]
+                n, c = poses.shape[0], poses.shape[1]
+                betas = betas.reshape(1, 300)
+                betas = np.tile(betas, (n, 1))
+                betas = torch.from_numpy(betas).cuda().float()
+                poses = torch.from_numpy(poses.reshape(n, c)).cuda().float()
+                exps = torch.from_numpy(exps.reshape(n, 100)).cuda().float()
+                trans = torch.from_numpy(trans.reshape(n, 3)).cuda().float()
+                max_length = 128
+                s, r = n//max_length, n%max_length
+                #print(n, s, r)
+                all_tensor = []
+                for i in range(s):
+                    with torch.no_grad():
+                        joints = self.smplx(
+                            betas=betas[i*max_length:(i+1)*max_length], 
+                            transl=trans[i*max_length:(i+1)*max_length], 
+                            expression=exps[i*max_length:(i+1)*max_length], 
+                            jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], 
+                            global_orient=poses[i*max_length:(i+1)*max_length,:3], 
+                            body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], 
+                            left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], 
+                            right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], 
+                            return_verts=True,
+                            return_joints=True,
+                            leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], 
+                            reye_pose=poses[i*max_length:(i+1)*max_length, 72:75],
+                        )['joints'][:, :55, :].reshape(max_length, 55*3)
+                    all_tensor.append(joints)
+                if r != 0:
+                    with torch.no_grad():
+                        joints = self.smplx(
+                            betas=betas[s*max_length:s*max_length+r], 
+                            transl=trans[s*max_length:s*max_length+r], 
+                            expression=exps[s*max_length:s*max_length+r], 
+                            jaw_pose=poses[s*max_length:s*max_length+r, 66:69], 
+                            global_orient=poses[s*max_length:s*max_length+r,:3], 
+                            body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], 
+                            left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], 
+                            right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], 
+                            return_verts=True,
+                            return_joints=True,
+                            leye_pose=poses[s*max_length:s*max_length+r, 69:72], 
+                            reye_pose=poses[s*max_length:s*max_length+r, 72:75],
+                        )['joints'][:, :55, :].reshape(r, 55*3)
+                    all_tensor.append(joints)
+                joints = torch.cat(all_tensor, axis=0)
+                joints = joints.permute(1, 0)
+                dt = 1/30
+            # first steps is forward diff (t+1 - t) / dt
+                init_vel = (joints[:, 1:2] - joints[:, :1]) / dt
+                # middle steps are second order (t+1 - t-1) / 2dt
+                middle_vel = (joints[:, 2:] - joints[:, 0:-2]) / (2 * dt)
+                # last step is backward diff (t - t-1) / dt
+                final_vel = (joints[:, -1:] - joints[:, -2:-1]) / dt
+                #print(joints.shape, init_vel.shape, middle_vel.shape, final_vel.shape)
+                vel_seq = torch.cat([init_vel, middle_vel, final_vel], dim=1).permute(1, 0).reshape(n, 55, 3)
+                #print(vel_seq.shape)
+                #.permute(1, 0).reshape(n, 55, 3)
+                vel_seq_np = vel_seq.cpu().numpy()
+                vel_joints_np = np.linalg.norm(vel_seq_np, axis=2) # n * 55
+                all_list.append(vel_joints_np)
+        avg_vel = np.mean(np.concatenate(all_list, axis=0),axis=0) # 55
+        np.save(save_path, avg_vel)
+        
+    
+    def build_cache(self, preloaded_dir):
+        logger.info(f"Audio bit rate: {self.args.audio_fps}")
+        logger.info("Reading data '{}'...".format(self.data_dir))
+        logger.info("Creating the dataset cache...")
+        if self.args.new_cache:
+            if os.path.exists(preloaded_dir):
+                shutil.rmtree(preloaded_dir)
+        if os.path.exists(preloaded_dir):
+            logger.info("Found the cache {}".format(preloaded_dir))
+        elif self.loader_type == "test":
+            self.cache_generation(
+                preloaded_dir, True, 
+                0, 0,
+                is_test=True)
+        else:
+            self.cache_generation(
+                preloaded_dir, self.args.disable_filtering, 
+                self.args.clean_first_seconds, self.args.clean_final_seconds,
+                is_test=False)
+        logger.info(f"BEATX during time is {self.beatx_during_time}s !")
+        logger.info(f"AMASS during time is {self.amass_during_time}s !")
+        
+        ## 对于BEATX train ,val ,test: 69800s ,7695s, 18672s ,总计 26.7h
+        ##
+        
+    def __len__(self):
+        return self.n_samples
+    
+
+    def cache_generation(self, out_lmdb_dir, disable_filtering, clean_first_seconds,  clean_final_seconds, is_test=False):
+        # if "wav2vec2" in self.args.audio_rep:
+        #     self.wav2vec_model = Wav2Vec2Model.from_pretrained(f"{self.args.data_path_1}/hub/transformer/wav2vec2-base-960h")
+        #     self.wav2vec_model.feature_extractor._freeze_parameters()
+        #     self.wav2vec_model = self.wav2vec_model.cuda()
+        #     self.wav2vec_model.eval()
+        
+        self.n_out_samples = 0
+        # create db for samples
+        if not os.path.exists(out_lmdb_dir): os.makedirs(out_lmdb_dir)
+        dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 50))# 50G
+        n_filtered_out = defaultdict(int)
+    
+        for index, file_name in self.selected_file.iterrows():
+            f_name = file_name["id"]
+            ext = ".npz" if "smplx" in self.args.pose_rep else ".bvh"
+            pose_file = self.data_dir + self.args.pose_rep + "/" + f_name + ext
+            pose_each_file = []
+            trans_each_file = []
+            trans_v_each_file = []
+            shape_each_file = []
+            audio_each_file = []
+            facial_each_file = []
+            word_each_file = []
+            emo_each_file = []
+            sem_each_file = []
+            vid_each_file = []
+            id_pose = f_name #1_wayne_0_1_1
+            
+            logger.info(colored(f"# ---- Building cache for Pose   {id_pose} ---- #", "blue"))
+            if "smplx" in self.args.pose_rep:
+                pose_data = np.load(pose_file, allow_pickle=True)
+                assert 30%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 30'
+                stride = int(30/self.args.pose_fps)
+                pose_each_file = pose_data["poses"][::stride] * self.joint_mask
+                pose_each_file = pose_each_file[:, self.joint_mask.astype(bool)]
+                # print(pose_each_file.shape)
+                self.beatx_during_time += pose_each_file.shape[0]/30
+                trans_each_file = pose_data["trans"][::stride]
+                trans_each_file[:,0] = trans_each_file[:,0] - trans_each_file[0,0]
+                trans_each_file[:,2] = trans_each_file[:,2] - trans_each_file[0,2]
+                trans_v_each_file = np.zeros_like(trans_each_file)
+                trans_v_each_file[1:,0] = trans_each_file[1:,0] - trans_each_file[:-1,0]
+                trans_v_each_file[0,0] = trans_v_each_file[1,0]
+                trans_v_each_file[1:,2] = trans_each_file[1:,2] - trans_each_file[:-1,2]
+                trans_v_each_file[0,2] = trans_v_each_file[1,2]
+                trans_v_each_file[:,1] = trans_each_file[:,1]
+                
+                
+                shape_each_file = np.repeat(pose_data["betas"].reshape(1, 300), pose_each_file.shape[0], axis=0)
+                if self.args.facial_rep is not None:
+                    logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #")
+                    facial_each_file = pose_data["expressions"][::stride]
+                    if self.args.facial_norm: 
+                        facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial
+                    
+            if self.args.id_rep is not None:
+                vid_each_file = np.repeat(np.array(int(f_name.split("_")[0])-1).reshape(1, 1), pose_each_file.shape[0], axis=0)
+      
+            filtered_result = self._sample_from_clip(
+                dst_lmdb_env,
+                pose_each_file, trans_each_file,trans_v_each_file, shape_each_file,
+                vid_each_file,
+                disable_filtering, clean_first_seconds, clean_final_seconds, is_test,
+                ) 
+            for type in filtered_result.keys():
+                n_filtered_out[type] += filtered_result[type]
+                    
+        if self.args.use_amass:
+            amass_dir = '/mnt/fu09a/chenbohong/PantoMatrix/scripts/EMAGE_2024/datasets/AMASS_SMPLX'
+            for dataset in amass_splits[self.loader_type]:
+                search_path = os.path.join(amass_dir,dataset, '**', '*.npz')
+                npz_files = glob.glob(search_path, recursive=True)     
+                for index, file_name in enumerate(npz_files):
+                    f_name = file_name.split('/')[-1]
+                    ext = ".npz" if "smplx" in self.args.pose_rep else ".bvh"
+                    pose_file = file_name
+                    pose_each_file = []
+                    trans_each_file = []
+                    trans_v_each_file = []
+                    shape_each_file = []
+                    audio_each_file = []
+                    facial_each_file = []
+                    word_each_file = []
+                    emo_each_file = []
+                    sem_each_file = []
+                    vid_each_file = []
+                    id_pose = f_name #1_wayne_0_1_1
+                    
+                    logger.info(colored(f"# ---- Building cache for Pose   {id_pose} ---- #", "blue"))
+                    if "smplx" in self.args.pose_rep:
+                        pose_data = np.load(pose_file, allow_pickle=True)
+                        if len(pose_data.files)==6:
+                            logger.info(colored(f"# ---- state file ---- #", "red"))
+                            continue
+                        assert 30%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 30'
+                        pose_each_file = self.load_amass(pose_data)
+                        fps = pose_data['mocap_frame_rate']
+                        stride =round(fps/30)
+                        pose_each_file = pose_data["poses"][::stride] * self.joint_mask
+                        pose_each_file = pose_each_file[:, self.joint_mask.astype(bool)]
+                        trans_each_file = pose_data["trans"][::stride]
+                        
+
+                        trans_each_file[:,0] = trans_each_file[:,0] - trans_each_file[0,0]
+                        trans_each_file[:,2] = trans_each_file[:,2] - trans_each_file[0,2]
+                        trans_v_each_file = np.zeros_like(trans_each_file)
+                        trans_v_each_file[1:,0] = trans_each_file[1:,0] - trans_each_file[:-1,0]
+                        trans_v_each_file[0,0] = trans_v_each_file[1,0]
+                        trans_v_each_file[1:,2] = trans_each_file[1:,2] - trans_each_file[:-1,2]
+                        trans_v_each_file[0,2] = trans_v_each_file[1,2]
+                        trans_v_each_file[:,1] = trans_each_file[:,1]
+                        
+                        
+                        
+                        shape_each_file = np.repeat(pose_data["betas"].reshape(1, -1), pose_each_file.shape[0], axis=0)
+                                
+                    if self.args.id_rep is not None:
+                        vid_each_file = np.repeat(np.array(int(100)-1).reshape(1, 1), pose_each_file.shape[0], axis=0)
+            
+                    filtered_result = self._sample_from_clip(
+                        dst_lmdb_env,
+                        pose_each_file, trans_each_file,trans_v_each_file, shape_each_file,
+                        vid_each_file,
+                        disable_filtering, clean_first_seconds, clean_final_seconds, is_test,
+                        ) 
+                    for type in filtered_result.keys():
+                        n_filtered_out[type] += filtered_result[type]
+                    
+                    
+        with dst_lmdb_env.begin() as txn:
+            logger.info(colored(f"no. of samples: {txn.stat()['entries']}", "cyan"))
+            n_total_filtered = 0
+            for type, n_filtered in n_filtered_out.items():
+                logger.info("{}: {}".format(type, n_filtered))
+                n_total_filtered += n_filtered
+            logger.info(colored("no. of excluded samples: {} ({:.1f}%)".format(
+                n_total_filtered, 100 * n_total_filtered / (txn.stat()["entries"] + n_total_filtered)), "cyan"))
+        dst_lmdb_env.sync()
+        dst_lmdb_env.close()
+    
+    def _sample_from_clip(
+        self, dst_lmdb_env, pose_each_file, trans_each_file, trans_v_each_file,shape_each_file,
+        vid_each_file,
+        disable_filtering, clean_first_seconds, clean_final_seconds, is_test,
+        ):
+        """
+        for data cleaning, we ignore the data for first and final n s
+        for test, we return all data 
+        """
+        # audio_start = int(self.alignment[0] * self.args.audio_fps)
+        # pose_start = int(self.alignment[1] * self.args.pose_fps)
+        #logger.info(f"before: {audio_each_file.shape} {pose_each_file.shape}")
+        # audio_each_file = audio_each_file[audio_start:]
+        # pose_each_file = pose_each_file[pose_start:]
+        # trans_each_file = 
+        #logger.info(f"after alignment: {audio_each_file.shape} {pose_each_file.shape}")
+        #print(pose_each_file.shape)
+        round_seconds_skeleton = pose_each_file.shape[0] // self.args.pose_fps  # assume 1500 frames / 15 fps = 100 s
+        #print(round_seconds_skeleton)
+ 
+        clip_s_t, clip_e_t = clean_first_seconds, round_seconds_skeleton - clean_final_seconds # assume [10, 90]s
+        clip_s_f_audio, clip_e_f_audio = self.args.audio_fps * clip_s_t, clip_e_t * self.args.audio_fps # [160,000,90*160,000]
+        clip_s_f_pose, clip_e_f_pose = clip_s_t * self.args.pose_fps, clip_e_t * self.args.pose_fps # [150,90*15]
+        
+        
+        for ratio in self.args.multi_length_training:
+            if is_test:# stride = length for test
+                cut_length = clip_e_f_pose - clip_s_f_pose
+                self.args.stride = cut_length
+                self.max_length = cut_length
+            else:
+                self.args.stride = int(ratio*self.ori_stride)
+                cut_length = int(self.ori_length*ratio)
+                
+            num_subdivision = math.floor((clip_e_f_pose - clip_s_f_pose - cut_length) / self.args.stride) + 1
+            logger.info(f"pose from frame {clip_s_f_pose} to {clip_e_f_pose}, length {cut_length}")
+            logger.info(f"{num_subdivision} clips is expected with stride {self.args.stride}")
+            
+
+            n_filtered_out = defaultdict(int)
+            sample_pose_list = []
+            sample_audio_list = []
+            sample_shape_list = []
+            sample_vid_list = []
+            sample_trans_list = []
+            sample_trans_v_list = []
+           
+            for i in range(num_subdivision): # cut into around 2s chip, (self npose)
+                start_idx = clip_s_f_pose + i * self.args.stride
+                fin_idx = start_idx + cut_length 
+                sample_pose = pose_each_file[start_idx:fin_idx]
+                sample_trans = trans_each_file[start_idx:fin_idx]
+                sample_trans_v = trans_v_each_file[start_idx:fin_idx]
+                sample_shape = shape_each_file[start_idx:fin_idx]
+                # print(sample_pose.shape)
+
+
+                sample_vid = vid_each_file[start_idx:fin_idx] if self.args.id_rep is not None else np.array([-1])
+                
+                if sample_pose.any() != None:
+                    # filtering motion skeleton data
+                    sample_pose, filtering_message = MotionPreprocessor(sample_pose).get()
+                    is_correct_motion = (sample_pose is not None)
+                    if is_correct_motion or disable_filtering:
+                        sample_pose_list.append(sample_pose)
+
+                        sample_shape_list.append(sample_shape)
+
+                        sample_vid_list.append(sample_vid)
+
+
+                        sample_trans_list.append(sample_trans)
+                        sample_trans_v_list.append(sample_trans_v)
+                    else:
+                        n_filtered_out[filtering_message] += 1
+
+            if len(sample_pose_list) > 0:
+                with dst_lmdb_env.begin(write=True) as txn:
+                    for pose, shape,  vid, trans,trans_v in zip(
+                        sample_pose_list,
+                        sample_shape_list,
+                        sample_vid_list,
+                        sample_trans_list,
+                        sample_trans_v_list,
+                        ):
+                        k = "{:005}".format(self.n_out_samples).encode("ascii")
+                        v = [pose , shape, vid, trans,trans_v]
+                        v = pickle.dumps(v,5)
+                        txn.put(k, v)
+                        self.n_out_samples += 1
+        return n_filtered_out
+
+    def __getitem__(self, idx):
+        with self.lmdb_env.begin(write=False) as txn:
+            key = "{:005}".format(idx).encode("ascii")
+            sample = txn.get(key)
+            sample = pickle.loads(sample)
+            tar_pose,  in_shape, vid, trans,trans_v = sample
+            tar_pose = torch.from_numpy(tar_pose).float()
+            tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(-1, 55, 3))
+            tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(-1, 55*6)
+            
+            if self.norm:
+                tar_pose = (tar_pose - self.mean) / self.std
+                trans_v = (trans_v-self.trans_mean)/self.trans_std
+            
+            if self.loader_type == "test":
+                tar_pose = tar_pose.float()
+                trans = torch.from_numpy(trans).float()
+                trans_v = torch.from_numpy(trans_v).float()
+                vid = torch.from_numpy(vid).float()
+                in_shape = torch.from_numpy(in_shape).float()
+            else:
+                in_shape = torch.from_numpy(in_shape).reshape((in_shape.shape[0], -1)).float()
+                trans = torch.from_numpy(trans).reshape((trans.shape[0], -1)).float()
+                trans_v = torch.from_numpy(trans_v).reshape((trans_v.shape[0], -1)).float()
+                vid = torch.from_numpy(vid).reshape((vid.shape[0], -1)).float()
+                tar_pose = tar_pose.reshape((tar_pose.shape[0], -1)).float()
+                tar_pose = torch.cat([tar_pose, trans_v], dim=1)
+            return tar_pose
+         
+class MotionPreprocessor:
+    def __init__(self, skeletons):
+        self.skeletons = skeletons
+        #self.mean_pose = mean_pose
+        self.filtering_message = "PASS"
+
+    def get(self):
+        assert (self.skeletons is not None)
+
+        # filtering
+        if self.skeletons is not None:
+            if self.check_pose_diff():
+                self.skeletons = []
+                self.filtering_message = "pose"
+            # elif self.check_spine_angle():
+            #     self.skeletons = []
+            #     self.filtering_message = "spine angle"
+            # elif self.check_static_motion():
+            #     self.skeletons = []
+            #     self.filtering_message = "motion"
+
+        # if self.skeletons != []:
+        #     self.skeletons = self.skeletons.tolist()
+        #     for i, frame in enumerate(self.skeletons):
+        #         assert not np.isnan(self.skeletons[i]).any()  # missing joints
+
+        return self.skeletons, self.filtering_message
+
+    def check_static_motion(self, verbose=True):
+        def get_variance(skeleton, joint_idx):
+            wrist_pos = skeleton[:, joint_idx]
+            variance = np.sum(np.var(wrist_pos, axis=0))
+            return variance
+
+        left_arm_var = get_variance(self.skeletons, 6)
+        right_arm_var = get_variance(self.skeletons, 9)
+
+        th = 0.0014  # exclude 13110
+        # th = 0.002  # exclude 16905
+        if left_arm_var < th and right_arm_var < th:
+            if verbose:
+                print("skip - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var))
+            return True
+        else:
+            if verbose:
+                print("pass - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var))
+            return False
+
+
+    def check_pose_diff(self, verbose=False):
+#         diff = np.abs(self.skeletons - self.mean_pose) # 186*1
+#         diff = np.mean(diff)
+
+#         # th = 0.017
+#         th = 0.02 #0.02  # exclude 3594
+#         if diff < th:
+#             if verbose:
+#                 print("skip - check_pose_diff {:.5f}".format(diff))
+#             return True
+# #         th = 3.5 #0.02  # exclude 3594
+# #         if 3.5 < diff < 5:
+# #             if verbose:
+# #                 print("skip - check_pose_diff {:.5f}".format(diff))
+# #             return True
+#         else:
+#             if verbose:
+#                 print("pass - check_pose_diff {:.5f}".format(diff))
+        return False
+
+
+    def check_spine_angle(self, verbose=True):
+        def angle_between(v1, v2):
+            v1_u = v1 / np.linalg.norm(v1)
+            v2_u = v2 / np.linalg.norm(v2)
+            return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0))
+
+        angles = []
+        for i in range(self.skeletons.shape[0]):
+            spine_vec = self.skeletons[i, 1] - self.skeletons[i, 0]
+            angle = angle_between(spine_vec, [0, -1, 0])
+            angles.append(angle)
+
+        if np.rad2deg(max(angles)) > 30 or np.rad2deg(np.mean(angles)) > 20:  # exclude 4495
+        # if np.rad2deg(max(angles)) > 20:  # exclude 8270
+            if verbose:
+                print("skip - check_spine_angle {:.5f}, {:.5f}".format(max(angles), np.mean(angles)))
+            return True
+        else:
+            if verbose:
+                print("pass - check_spine_angle {:.5f}".format(max(angles)))
+            return False

dataloaders/pymo/Quaternions.py

@@ -0,0 +1,468 @@
+import numpy as np
+
+class Quaternions:
+    """
+    Quaternions is a wrapper around a numpy ndarray
+    that allows it to act as if it were an narray of
+    a quaternion data type.
+    
+    Therefore addition, subtraction, multiplication,
+    division, negation, absolute, are all defined
+    in terms of quaternion operations such as quaternion
+    multiplication.
+    
+    This allows for much neater code and many routines
+    which conceptually do the same thing to be written
+    in the same way for point data and for rotation data.
+    
+    The Quaternions class has been desgined such that it
+    should support broadcasting and slicing in all of the
+    usual ways.
+    """
+    
+    def __init__(self, qs):
+        if isinstance(qs, np.ndarray):
+        
+            if len(qs.shape) == 1: qs = np.array([qs])
+            self.qs = qs
+            return
+            
+        if isinstance(qs, Quaternions):
+            self.qs = qs.qs
+            return
+            
+        raise TypeError('Quaternions must be constructed from iterable, numpy array, or Quaternions, not %s' % type(qs))
+    
+    def __str__(self): return "Quaternions("+ str(self.qs) + ")"
+    def __repr__(self): return "Quaternions("+ repr(self.qs) + ")"
+    
+    """ Helper Methods for Broadcasting and Data extraction """
+    
+    @classmethod
+    def _broadcast(cls, sqs, oqs, scalar=False):
+        
+        if isinstance(oqs, float): return sqs, oqs * np.ones(sqs.shape[:-1])
+        
+        ss = np.array(sqs.shape) if not scalar else np.array(sqs.shape[:-1])
+        os = np.array(oqs.shape)
+        
+        if len(ss) != len(os):
+            raise TypeError('Quaternions cannot broadcast together shapes %s and %s' % (sqs.shape, oqs.shape))
+            
+        if np.all(ss == os): return sqs, oqs
+        
+        if not np.all((ss == os) | (os == np.ones(len(os))) | (ss == np.ones(len(ss)))):
+            raise TypeError('Quaternions cannot broadcast together shapes %s and %s' % (sqs.shape, oqs.shape))
+            
+        sqsn, oqsn = sqs.copy(), oqs.copy()
+        
+        for a in np.where(ss == 1)[0]: sqsn = sqsn.repeat(os[a], axis=a)
+        for a in np.where(os == 1)[0]: oqsn = oqsn.repeat(ss[a], axis=a)
+        
+        return sqsn, oqsn
+        
+    """ Adding Quaterions is just Defined as Multiplication """
+    
+    def __add__(self, other): return self * other
+    def __sub__(self, other): return self / other
+    
+    """ Quaterion Multiplication """
+    
+    def __mul__(self, other):
+        """
+        Quaternion multiplication has three main methods.
+        
+        When multiplying a Quaternions array by Quaternions
+        normal quaternion multiplication is performed.
+        
+        When multiplying a Quaternions array by a vector
+        array of the same shape, where the last axis is 3,
+        it is assumed to be a Quaternion by 3D-Vector 
+        multiplication and the 3D-Vectors are rotated
+        in space by the Quaternions.
+        
+        When multipplying a Quaternions array by a scalar
+        or vector of different shape it is assumed to be
+        a Quaternions by Scalars multiplication and the
+        Quaternions are scaled using Slerp and the identity
+        quaternions.
+        """
+        
+        """ If Quaternions type do Quaternions * Quaternions """
+        if isinstance(other, Quaternions):
+            
+            sqs, oqs = Quaternions._broadcast(self.qs, other.qs)
+            
+            q0 = sqs[...,0]; q1 = sqs[...,1]; 
+            q2 = sqs[...,2]; q3 = sqs[...,3]; 
+            r0 = oqs[...,0]; r1 = oqs[...,1]; 
+            r2 = oqs[...,2]; r3 = oqs[...,3]; 
+            
+            qs = np.empty(sqs.shape)
+            qs[...,0] = r0 * q0 - r1 * q1 - r2 * q2 - r3 * q3
+            qs[...,1] = r0 * q1 + r1 * q0 - r2 * q3 + r3 * q2
+            qs[...,2] = r0 * q2 + r1 * q3 + r2 * q0 - r3 * q1
+            qs[...,3] = r0 * q3 - r1 * q2 + r2 * q1 + r3 * q0
+            
+            return Quaternions(qs)
+        
+        """ If array type do Quaternions * Vectors """
+        if isinstance(other, np.ndarray) and other.shape[-1] == 3:
+            vs = Quaternions(np.concatenate([np.zeros(other.shape[:-1] + (1,)), other], axis=-1))
+            return (self * (vs * -self)).imaginaries
+        
+        """ If float do Quaternions * Scalars """
+        if isinstance(other, np.ndarray) or isinstance(other, float):
+            return Quaternions.slerp(Quaternions.id_like(self), self, other)
+        
+        raise TypeError('Cannot multiply/add Quaternions with type %s' % str(type(other)))
+        
+    def __div__(self, other):
+        """
+        When a Quaternion type is supplied, division is defined
+        as multiplication by the inverse of that Quaternion.
+        
+        When a scalar or vector is supplied it is defined
+        as multiplicaion of one over the supplied value.
+        Essentially a scaling.
+        """
+        
+        if isinstance(other, Quaternions): return self * (-other)
+        if isinstance(other, np.ndarray): return self * (1.0 / other)
+        if isinstance(other, float): return self * (1.0 / other)
+        raise TypeError('Cannot divide/subtract Quaternions with type %s' + str(type(other)))
+        
+    def __eq__(self, other): return self.qs == other.qs
+    def __ne__(self, other): return self.qs != other.qs
+    
+    def __neg__(self):
+        """ Invert Quaternions """
+        return Quaternions(self.qs * np.array([[1, -1, -1, -1]]))
+    
+    def __abs__(self):
+        """ Unify Quaternions To Single Pole """
+        qabs = self.normalized().copy()
+        top = np.sum(( qabs.qs) * np.array([1,0,0,0]), axis=-1)
+        bot = np.sum((-qabs.qs) * np.array([1,0,0,0]), axis=-1)
+        qabs.qs[top < bot] = -qabs.qs[top <  bot]
+        return qabs
+    
+    def __iter__(self): return iter(self.qs)
+    def __len__(self): return len(self.qs)
+    
+    def __getitem__(self, k):    return Quaternions(self.qs[k]) 
+    def __setitem__(self, k, v): self.qs[k] = v.qs
+        
+    @property
+    def lengths(self):
+        return np.sum(self.qs**2.0, axis=-1)**0.5
+    
+    @property
+    def reals(self):
+        return self.qs[...,0]
+        
+    @property
+    def imaginaries(self):
+        return self.qs[...,1:4]
+    
+    @property
+    def shape(self): return self.qs.shape[:-1]
+    
+    def repeat(self, n, **kwargs):
+        return Quaternions(self.qs.repeat(n, **kwargs))
+    
+    def normalized(self):
+        return Quaternions(self.qs / self.lengths[...,np.newaxis])
+    
+    def log(self):
+        norm = abs(self.normalized())
+        imgs = norm.imaginaries
+        lens = np.sqrt(np.sum(imgs**2, axis=-1))
+        lens = np.arctan2(lens, norm.reals) / (lens + 1e-10)
+        return imgs * lens[...,np.newaxis]
+    
+    def constrained(self, axis):
+        
+        rl = self.reals
+        im = np.sum(axis * self.imaginaries, axis=-1)
+        
+        t1 = -2 * np.arctan2(rl, im) + np.pi
+        t2 = -2 * np.arctan2(rl, im) - np.pi
+        
+        top = Quaternions.exp(axis[np.newaxis] * (t1[:,np.newaxis] / 2.0))
+        bot = Quaternions.exp(axis[np.newaxis] * (t2[:,np.newaxis] / 2.0))
+        img = self.dot(top) > self.dot(bot)
+        
+        ret = top.copy()
+        ret[ img] = top[ img]
+        ret[~img] = bot[~img]
+        return ret
+    
+    def constrained_x(self): return self.constrained(np.array([1,0,0]))
+    def constrained_y(self): return self.constrained(np.array([0,1,0]))
+    def constrained_z(self): return self.constrained(np.array([0,0,1]))
+    
+    def dot(self, q): return np.sum(self.qs * q.qs, axis=-1)
+    
+    def copy(self): return Quaternions(np.copy(self.qs))
+    
+    def reshape(self, s):
+        self.qs.reshape(s)
+        return self
+    
+    def interpolate(self, ws):
+        return Quaternions.exp(np.average(abs(self).log, axis=0, weights=ws))
+    
+    def euler(self, order='xyz'):
+        
+        q = self.normalized().qs
+        q0 = q[...,0]
+        q1 = q[...,1]
+        q2 = q[...,2]
+        q3 = q[...,3]
+        es = np.zeros(self.shape + (3,))
+        
+        if   order == 'xyz':
+            es[...,0] = np.arctan2(2 * (q0 * q1 + q2 * q3), 1 - 2 * (q1 * q1 + q2 * q2))
+            es[...,1] = np.arcsin((2 * (q0 * q2 - q3 * q1)).clip(-1,1))
+            es[...,2] = np.arctan2(2 * (q0 * q3 + q1 * q2), 1 - 2 * (q2 * q2 + q3 * q3))
+        elif order == 'yzx':
+            es[...,0] = np.arctan2(2 * (q1 * q0 - q2 * q3), -q1 * q1 + q2 * q2 - q3 * q3 + q0 * q0)
+            es[...,1] = np.arctan2(2 * (q2 * q0 - q1 * q3),  q1 * q1 - q2 * q2 - q3 * q3 + q0 * q0)
+            es[...,2] = np.arcsin((2 * (q1 * q2 + q3 * q0)).clip(-1,1))
+        else:
+            raise NotImplementedError('Cannot convert from ordering %s' % order)
+        
+        """
+        
+        # These conversion don't appear to work correctly for Maya.
+        # http://bediyap.com/programming/convert-quaternion-to-euler-rotations/
+        
+        if   order == 'xyz':
+            es[...,0] = np.arctan2(2 * (q0 * q3 - q1 * q2), q0 * q0 + q1 * q1 - q2 * q2 - q3 * q3)
+            es[...,1] = np.arcsin((2 * (q1 * q3 + q0 * q2)).clip(-1,1))
+            es[...,2] = np.arctan2(2 * (q0 * q1 - q2 * q3), q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3)
+        elif order == 'yzx':
+            es[...,0] = np.arctan2(2 * (q0 * q1 - q2 * q3), q0 * q0 - q1 * q1 + q2 * q2 - q3 * q3)
+            es[...,1] = np.arcsin((2 * (q1 * q2 + q0 * q3)).clip(-1,1))
+            es[...,2] = np.arctan2(2 * (q0 * q2 - q1 * q3), q0 * q0 + q1 * q1 - q2 * q2 - q3 * q3)
+        elif order == 'zxy':
+            es[...,0] = np.arctan2(2 * (q0 * q2 - q1 * q3), q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3)
+            es[...,1] = np.arcsin((2 * (q0 * q1 + q2 * q3)).clip(-1,1))
+            es[...,2] = np.arctan2(2 * (q0 * q3 - q1 * q2), q0 * q0 - q1 * q1 + q2 * q2 - q3 * q3) 
+        elif order == 'xzy':
+            es[...,0] = np.arctan2(2 * (q0 * q2 + q1 * q3), q0 * q0 + q1 * q1 - q2 * q2 - q3 * q3)
+            es[...,1] = np.arcsin((2 * (q0 * q3 - q1 * q2)).clip(-1,1))
+            es[...,2] = np.arctan2(2 * (q0 * q1 + q2 * q3), q0 * q0 - q1 * q1 + q2 * q2 - q3 * q3)
+        elif order == 'yxz':
+            es[...,0] = np.arctan2(2 * (q1 * q2 + q0 * q3), q0 * q0 - q1 * q1 + q2 * q2 - q3 * q3)
+            es[...,1] = np.arcsin((2 * (q0 * q1 - q2 * q3)).clip(-1,1))
+            es[...,2] = np.arctan2(2 * (q1 * q3 + q0 * q2), q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3)
+        elif order == 'zyx':
+            es[...,0] = np.arctan2(2 * (q0 * q1 + q2 * q3), q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3)
+            es[...,1] = np.arcsin((2 * (q0 * q2 - q1 * q3)).clip(-1,1))
+            es[...,2] = np.arctan2(2 * (q0 * q3 + q1 * q2), q0 * q0 + q1 * q1 - q2 * q2 - q3 * q3)
+        else:
+            raise KeyError('Unknown ordering %s' % order)
+        
+        """
+        
+        # https://github.com/ehsan/ogre/blob/master/OgreMain/src/OgreMatrix3.cpp
+        # Use this class and convert from matrix
+        
+        return es
+        
+    
+    def average(self):
+        
+        if len(self.shape) == 1:
+            
+            import numpy.core.umath_tests as ut
+            system = ut.matrix_multiply(self.qs[:,:,np.newaxis], self.qs[:,np.newaxis,:]).sum(axis=0)
+            w, v = np.linalg.eigh(system)
+            qiT_dot_qref = (self.qs[:,:,np.newaxis] * v[np.newaxis,:,:]).sum(axis=1)
+            return Quaternions(v[:,np.argmin((1.-qiT_dot_qref**2).sum(axis=0))])            
+        
+        else:
+            
+            raise NotImplementedError('Cannot average multi-dimensionsal Quaternions')
+
+    def angle_axis(self):
+        
+        norm = self.normalized()        
+        s = np.sqrt(1 - (norm.reals**2.0))
+        s[s == 0] = 0.001
+        
+        angles = 2.0 * np.arccos(norm.reals)
+        axis = norm.imaginaries / s[...,np.newaxis]
+        
+        return angles, axis
+        
+    
+    def transforms(self):
+        
+        qw = self.qs[...,0]
+        qx = self.qs[...,1]
+        qy = self.qs[...,2]
+        qz = self.qs[...,3]
+        
+        x2 = qx + qx; y2 = qy + qy; z2 = qz + qz;
+        xx = qx * x2; yy = qy * y2; wx = qw * x2;
+        xy = qx * y2; yz = qy * z2; wy = qw * y2;
+        xz = qx * z2; zz = qz * z2; wz = qw * z2;
+        
+        m = np.empty(self.shape + (3,3))
+        m[...,0,0] = 1.0 - (yy + zz)
+        m[...,0,1] = xy - wz
+        m[...,0,2] = xz + wy        
+        m[...,1,0] = xy + wz
+        m[...,1,1] = 1.0 - (xx + zz)
+        m[...,1,2] = yz - wx        
+        m[...,2,0] = xz - wy
+        m[...,2,1] = yz + wx
+        m[...,2,2] = 1.0 - (xx + yy)
+        
+        return m
+    
+    def ravel(self):
+        return self.qs.ravel()
+    
+    @classmethod
+    def id(cls, n):
+        
+        if isinstance(n, tuple):
+            qs = np.zeros(n + (4,))
+            qs[...,0] = 1.0
+            return Quaternions(qs)
+        
+        if isinstance(n, int) or isinstance(n, long):
+            qs = np.zeros((n,4))
+            qs[:,0] = 1.0
+            return Quaternions(qs)
+        
+        raise TypeError('Cannot Construct Quaternion from %s type' % str(type(n)))
+
+    @classmethod
+    def id_like(cls, a):
+        qs = np.zeros(a.shape + (4,))
+        qs[...,0] = 1.0
+        return Quaternions(qs)
+        
+    @classmethod
+    def exp(cls, ws):
+    
+        ts = np.sum(ws**2.0, axis=-1)**0.5
+        ts[ts == 0] = 0.001
+        ls = np.sin(ts) / ts
+        
+        qs = np.empty(ws.shape[:-1] + (4,))
+        qs[...,0] = np.cos(ts)
+        qs[...,1] = ws[...,0] * ls
+        qs[...,2] = ws[...,1] * ls
+        qs[...,3] = ws[...,2] * ls
+        
+        return Quaternions(qs).normalized()
+        
+    @classmethod
+    def slerp(cls, q0s, q1s, a):
+        
+        fst, snd = cls._broadcast(q0s.qs, q1s.qs)
+        fst, a = cls._broadcast(fst, a, scalar=True)
+        snd, a = cls._broadcast(snd, a, scalar=True)
+        
+        len = np.sum(fst * snd, axis=-1)
+        
+        neg = len < 0.0
+        len[neg] = -len[neg]
+        snd[neg] = -snd[neg]
+        
+        amount0 = np.zeros(a.shape)
+        amount1 = np.zeros(a.shape)
+
+        linear = (1.0 - len) < 0.01
+        omegas = np.arccos(len[~linear])
+        sinoms = np.sin(omegas)
+        
+        amount0[ linear] = 1.0 - a[linear]
+        amount1[ linear] =       a[linear]
+        amount0[~linear] = np.sin((1.0 - a[~linear]) * omegas) / sinoms
+        amount1[~linear] = np.sin(       a[~linear]  * omegas) / sinoms
+        
+        return Quaternions(
+            amount0[...,np.newaxis] * fst + 
+            amount1[...,np.newaxis] * snd)
+    
+    @classmethod
+    def between(cls, v0s, v1s):
+        a = np.cross(v0s, v1s)
+        w = np.sqrt((v0s**2).sum(axis=-1) * (v1s**2).sum(axis=-1)) + (v0s * v1s).sum(axis=-1)
+        return Quaternions(np.concatenate([w[...,np.newaxis], a], axis=-1)).normalized()
+    
+    @classmethod
+    def from_angle_axis(cls, angles, axis):
+        axis    = axis / (np.sqrt(np.sum(axis**2, axis=-1)) + 1e-10)[...,np.newaxis]
+        sines   = np.sin(angles / 2.0)[...,np.newaxis]
+        cosines = np.cos(angles / 2.0)[...,np.newaxis]
+        return Quaternions(np.concatenate([cosines, axis * sines], axis=-1))
+    
+    @classmethod
+    def from_euler(cls, es, order='xyz', world=False):
+    
+        axis = {
+            'x' : np.array([1,0,0]),
+            'y' : np.array([0,1,0]),
+            'z' : np.array([0,0,1]),
+        }
+        
+        q0s = Quaternions.from_angle_axis(es[...,0], axis[order[0]])
+        q1s = Quaternions.from_angle_axis(es[...,1], axis[order[1]])
+        q2s = Quaternions.from_angle_axis(es[...,2], axis[order[2]])
+        
+        return (q2s * (q1s * q0s)) if world else (q0s * (q1s * q2s))
+    
+    @classmethod
+    def from_transforms(cls, ts):
+        
+        d0, d1, d2 = ts[...,0,0], ts[...,1,1], ts[...,2,2]
+        
+        q0 = ( d0 + d1 + d2 + 1.0) / 4.0
+        q1 = ( d0 - d1 - d2 + 1.0) / 4.0
+        q2 = (-d0 + d1 - d2 + 1.0) / 4.0
+        q3 = (-d0 - d1 + d2 + 1.0) / 4.0
+        
+        q0 = np.sqrt(q0.clip(0,None))
+        q1 = np.sqrt(q1.clip(0,None))
+        q2 = np.sqrt(q2.clip(0,None))
+        q3 = np.sqrt(q3.clip(0,None))
+        
+        c0 = (q0 >= q1) & (q0 >= q2) & (q0 >= q3)
+        c1 = (q1 >= q0) & (q1 >= q2) & (q1 >= q3)
+        c2 = (q2 >= q0) & (q2 >= q1) & (q2 >= q3)
+        c3 = (q3 >= q0) & (q3 >= q1) & (q3 >= q2)
+        
+        q1[c0] *= np.sign(ts[c0,2,1] - ts[c0,1,2])
+        q2[c0] *= np.sign(ts[c0,0,2] - ts[c0,2,0])
+        q3[c0] *= np.sign(ts[c0,1,0] - ts[c0,0,1])
+        
+        q0[c1] *= np.sign(ts[c1,2,1] - ts[c1,1,2])
+        q2[c1] *= np.sign(ts[c1,1,0] + ts[c1,0,1])
+        q3[c1] *= np.sign(ts[c1,0,2] + ts[c1,2,0])  
+        
+        q0[c2] *= np.sign(ts[c2,0,2] - ts[c2,2,0])
+        q1[c2] *= np.sign(ts[c2,1,0] + ts[c2,0,1])
+        q3[c2] *= np.sign(ts[c2,2,1] + ts[c2,1,2])  
+        
+        q0[c3] *= np.sign(ts[c3,1,0] - ts[c3,0,1])
+        q1[c3] *= np.sign(ts[c3,2,0] + ts[c3,0,2])
+        q2[c3] *= np.sign(ts[c3,2,1] + ts[c3,1,2])  
+        
+        qs = np.empty(ts.shape[:-2] + (4,))
+        qs[...,0] = q0
+        qs[...,1] = q1
+        qs[...,2] = q2
+        qs[...,3] = q3
+        
+        return cls(qs)
+        
+    
+    

dataloaders/pymo/data.py

@@ -0,0 +1,53 @@
+import numpy as np
+
+class Joint():
+    def __init__(self, name, parent=None, children=None):
+        self.name = name
+        self.parent = parent
+        self.children = children
+
+class MocapData():
+    def __init__(self):
+        self.skeleton = {}
+        self.values = None
+        self.channel_names = []
+        self.framerate = 0.0
+        self.root_name = ''
+    
+    def traverse(self, j=None):
+        stack = [self.root_name]
+        while stack:
+            joint = stack.pop()
+            yield joint
+            for c in self.skeleton[joint]['children']:
+                stack.append(c)
+
+    def clone(self):
+        import copy
+        new_data = MocapData()
+        new_data.skeleton = copy.copy(self.skeleton)
+        new_data.values = copy.copy(self.values)
+        new_data.channel_names = copy.copy(self.channel_names)
+        new_data.root_name = copy.copy(self.root_name)
+        new_data.framerate = copy.copy(self.framerate)
+        return new_data
+
+    def get_all_channels(self):
+        '''Returns all of the channels parsed from the file as a 2D numpy array'''
+
+        frames = [f[1] for f in self.values]
+        return np.asarray([[channel[2] for channel in frame] for frame in frames])
+
+    def get_skeleton_tree(self):
+        tree = []
+        root_key =  [j for j in self.skeleton if self.skeleton[j]['parent']==None][0]
+        
+        root_joint = Joint(root_key)
+    
+    def get_empty_channels(self):
+        #TODO
+        pass
+
+    def get_constant_channels(self):
+        #TODO
+        pass

dataloaders/pymo/features.py

@@ -0,0 +1,43 @@
+'''
+A set of mocap feature extraction functions
+
+Created by Omid Alemi | Nov 17 2017
+
+'''
+import numpy as np
+import pandas as pd
+import peakutils
+import matplotlib.pyplot as plt
+
+def get_foot_contact_idxs(signal, t=0.02, min_dist=120):
+    up_idxs = peakutils.indexes(signal, thres=t/max(signal), min_dist=min_dist)
+    down_idxs = peakutils.indexes(-signal, thres=t/min(signal), min_dist=min_dist)
+
+    return [up_idxs, down_idxs]
+
+
+def create_foot_contact_signal(mocap_track, col_name, start=1, t=0.02, min_dist=120):
+    signal = mocap_track.values[col_name].values
+    idxs = get_foot_contact_idxs(signal, t, min_dist)
+
+    step_signal = []
+
+    c = start
+    for f in range(len(signal)):    
+        if f in idxs[1]:
+            c = 0
+        elif f in idxs[0]:
+            c = 1
+                                            
+        step_signal.append(c)
+    
+    return step_signal
+
+def plot_foot_up_down(mocap_track, col_name, t=0.02, min_dist=120):
+    
+    signal = mocap_track.values[col_name].values
+    idxs = get_foot_contact_idxs(signal, t, min_dist)
+
+    plt.plot(mocap_track.values.index, signal)
+    plt.plot(mocap_track.values.index[idxs[0]], signal[idxs[0]], 'ro')
+    plt.plot(mocap_track.values.index[idxs[1]], signal[idxs[1]], 'go')

dataloaders/pymo/mocapplayer/data-template.js

@@ -0,0 +1,3 @@
+var dataBuffer = `$$DATA$$`;
+
+start(dataBuffer);

dataloaders/pymo/mocapplayer/js/skeletonFactory.js

@@ -0,0 +1,233 @@
+bm_v = new THREE.MeshPhongMaterial({
+    color: 0x08519c,
+    emissive: 0x08306b,
+    specular: 0x08519c,
+    shininess: 10,
+    side: THREE.DoubleSide
+});
+
+jm_v = new THREE.MeshPhongMaterial({
+    color: 0x08306b,
+    emissive: 0x000000,
+    specular: 0x111111,
+    shininess: 90,
+    side: THREE.DoubleSide
+});
+
+bm_a = new THREE.MeshPhongMaterial({
+    color: 0x980043,
+    emissive: 0x67001f,
+    specular: 0x6a51a3,
+    shininess: 10,
+    side: THREE.DoubleSide
+});
+
+jm_a = new THREE.MeshPhongMaterial({
+    color: 0x67001f,
+    emissive: 0x000000,
+    specular: 0x111111,
+    shininess: 90,
+    side: THREE.DoubleSide
+});
+
+bm_b = new THREE.MeshPhongMaterial({
+    color: 0x3f007d,
+    emissive: 0x3f007d,
+    specular: 0x807dba,
+    shininess: 2,
+    side: THREE.DoubleSide
+});
+
+jm_b = new THREE.MeshPhongMaterial({
+    color: 0x3f007d,
+    emissive: 0x000000,
+    specular: 0x807dba,
+    shininess: 90,
+    side: THREE.DoubleSide
+});
+
+//------------------
+
+
+jointmaterial = new THREE.MeshLambertMaterial({
+    color: 0xc57206,
+    emissive: 0x271c18,
+    side: THREE.DoubleSide,
+    // shading: THREE.FlatShading,
+    wireframe: false,
+    shininess: 90,
+});
+
+bonematerial = new THREE.MeshPhongMaterial({
+    color: 0xbd9a6d,
+    emissive: 0x271c18,
+    side: THREE.DoubleSide,
+    // shading: THREE.FlatShading,
+    wireframe: false
+});
+
+jointmaterial2 = new THREE.MeshPhongMaterial({
+    color: 0x1562a2,
+    emissive: 0x000000,
+    specular: 0x111111,
+    shininess: 30,
+    side: THREE.DoubleSide
+});
+
+bonematerial2 = new THREE.MeshPhongMaterial({
+    color: 0x552211,
+    emissive: 0x882211,
+    // emissive: 0x000000,
+    specular: 0x111111,
+    shininess: 30,
+    side: THREE.DoubleSide
+});
+
+bonematerial3 = new THREE.MeshPhongMaterial({
+    color: 0x176793,
+    emissive: 0x000000,
+    specular: 0x111111,
+    shininess: 90,
+    side: THREE.DoubleSide
+});
+
+
+
+jointmaterial4 = new THREE.MeshPhongMaterial({
+    color: 0xFF8A00,
+    emissive: 0x000000,
+    specular: 0x111111,
+    shininess: 90,
+    side: THREE.DoubleSide
+});
+
+
+bonematerial4 = new THREE.MeshPhongMaterial({
+    color: 0x53633D,
+    emissive: 0x000000,
+    specular: 0xFFC450,
+    shininess: 90,
+    side: THREE.DoubleSide
+});
+
+
+
+bonematerial44 = new THREE.MeshPhongMaterial({
+    color: 0x582A72,
+    emissive: 0x000000,
+    specular: 0xFFC450,
+    shininess: 90,
+    side: THREE.DoubleSide
+});
+
+jointmaterial5 = new THREE.MeshPhongMaterial({
+    color: 0xAA5533,
+    emissive: 0x000000,
+    specular: 0x111111,
+    shininess: 30,
+    side: THREE.DoubleSide
+});
+
+bonematerial5 = new THREE.MeshPhongMaterial({
+    color: 0x552211,
+    emissive: 0x772211,
+    specular: 0x111111,
+    shininess: 30,
+    side: THREE.DoubleSide
+});
+
+
+markermaterial = new THREE.MeshPhongMaterial({
+    color: 0xc57206,
+    emissive: 0x271c18,
+    side: THREE.DoubleSide,
+    // shading: THREE.FlatShading,
+    wireframe: false,
+    shininess: 20,
+});
+
+markermaterial2 = new THREE.MeshPhongMaterial({
+    color: 0x1562a2,
+    emissive: 0x271c18,
+    side: THREE.DoubleSide,
+    // shading: THREE.FlatShading,
+    wireframe: false,
+    shininess: 20,
+});
+
+markermaterial3 = new THREE.MeshPhongMaterial({
+    color: 0x555555,
+    emissive: 0x999999,
+    side: THREE.DoubleSide,
+    // shading: THREE.FlatShading,
+    wireframe: false,
+    shininess: 20,
+});
+
+
+var makeMarkerGeometry_Sphere10 = function(markerName, scale) {
+    return new THREE.SphereGeometry(10, 60, 60);
+};
+
+var makeMarkerGeometry_Sphere3 = function(markerName, scale) {
+    return new THREE.SphereGeometry(3, 60, 60);
+};
+
+var makeMarkerGeometry_SphereX = function(markerName, scale) {
+    return new THREE.SphereGeometry(5, 60, 60);
+};
+
+var makeJointGeometry_SphereX = function(X) {
+    return function(jointName, scale) {
+        return new THREE.SphereGeometry(X, 60, 60);
+    };    
+};
+
+
+var makeJointGeometry_Sphere1 = function(jointName, scale) {
+    return new THREE.SphereGeometry(2 / scale, 60, 60);
+};
+
+var makeJointGeometry_Sphere2 = function(jointName, scale) {
+    return new THREE.SphereGeometry(1 / scale, 60, 60);
+};
+
+var makeJointGeometry_Dode = function(jointName, scale) {
+    return new THREE.DodecahedronGeometry(1 / scale, 0);
+};
+
+var makeBoneGeometry_Cylinder1 = function(joint1Name, joint2Name, length, scale) {
+    return new THREE.CylinderGeometry(1.5 / scale, 0.7 / scale, length, 40);
+};
+
+var makeBoneGeometry_Cylinder2 = function(joint1Name, joint2Name, length, scale) {
+    // if (joint1Name.includes("LeftHip"))
+    //     length = 400;
+    return new THREE.CylinderGeometry(1.5 / scale, 0.2 / scale, length, 40);   
+};
+
+var makeBoneGeometry_Cylinder3 = function(joint1Name, joint2Name, length, scale) {
+    var c1 = new THREE.CylinderGeometry(1.5 / scale, 0.2 / scale, length / 1, 20);
+    var c2 = new THREE.CylinderGeometry(0.2 / scale, 1.5 / scale, length / 1, 40);
+
+    var material = new THREE.MeshPhongMaterial({
+        color: 0xF7FE2E
+    });
+    var mmesh = new THREE.Mesh(c1, material);
+    mmesh.updateMatrix();
+    c2.merge(mmesh.geometry, mmesh.matrix);
+    return c2;
+};
+
+var makeBoneGeometry_Box1 = function(joint1Name, joint2Name, length, scale) {
+    return new THREE.BoxGeometry(1 / scale, length, 1 / scale, 40);
+};
+
+
+var makeJointGeometry_Empty = function(jointName, scale) {
+    return new THREE.SphereGeometry(0.001, 60, 60);
+};
+
+var makeBoneGeometry_Empty = function(joint1Name, joint2Name, length, scale) {
+    return new THREE.CylinderGeometry(0.001, 0.001, 0.001, 40);
+};

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dataloaders/pymo/mocapplayer/libs/mocapjs.js

@@ -0,0 +1,1312 @@
+/*!
+ * The MIT License (MIT)
+ * 
+ * Copyright (c) 2016 Omid Alemi
+ * 
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ * 
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ * 
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ * 
+ */
+/******/ (function(modules) { // webpackBootstrap
+/******/ 	// The module cache
+/******/ 	var installedModules = {};
+/******/
+/******/ 	// The require function
+/******/ 	function __webpack_require__(moduleId) {
+/******/
+/******/ 		// Check if module is in cache
+/******/ 		if(installedModules[moduleId])
+/******/ 			return installedModules[moduleId].exports;
+/******/
+/******/ 		// Create a new module (and put it into the cache)
+/******/ 		var module = installedModules[moduleId] = {
+/******/ 			exports: {},
+/******/ 			id: moduleId,
+/******/ 			loaded: false
+/******/ 		};
+/******/
+/******/ 		// Execute the module function
+/******/ 		modules[moduleId].call(module.exports, module, module.exports, __webpack_require__);
+/******/
+/******/ 		// Flag the module as loaded
+/******/ 		module.loaded = true;
+/******/
+/******/ 		// Return the exports of the module
+/******/ 		return module.exports;
+/******/ 	}
+/******/
+/******/
+/******/ 	// expose the modules object (__webpack_modules__)
+/******/ 	__webpack_require__.m = modules;
+/******/
+/******/ 	// expose the module cache
+/******/ 	__webpack_require__.c = installedModules;
+/******/
+/******/ 	// __webpack_public_path__
+/******/ 	__webpack_require__.p = "";
+/******/
+/******/ 	// Load entry module and return exports
+/******/ 	return __webpack_require__(0);
+/******/ })
+/************************************************************************/
+/******/ ([
+/* 0 */
+/***/ function(module, exports, __webpack_require__) {
+
+	BVHCharacter = __webpack_require__(1);
+	C3DCharacter = __webpack_require__(5);
+	MocapParsers = __webpack_require__(2);
+
+/***/ },
+/* 1 */
+/***/ function(module, exports, __webpack_require__) {
+
+	var parsers = __webpack_require__(2);
+	
+	var BVHCharacter = BVHCharacter || {};
+	
+	
+	BVHCharacter = function(n, jm, bm, jg, bg) {
+	    this.name = n;
+	
+	    this.jointMaterial = jm;
+	    this.boneMaterial = bm;
+	    this.makeJointGeometryFCN = jg;
+	    this.makeBoneGeometryFCN = bg;
+	
+	    this.bvh = [];
+	    this.skeleton = new THREE.Group();
+	
+	    this.skelScale = 1;
+	    this.jointMeshes = [];
+	    this.boneMeshes = [];
+	    this.rootMeshes = [];
+	
+	    this.originPosition = new THREE.Vector3(0, 0, 0);
+	
+	    this.ready = false;
+	    this.frameTime = 1 / 30;
+	    this.frameCount = 0;
+	    this.animIndex = 0;
+	    this.animStartTimeRef = 0;
+	    this.animOffset = 0;
+	    this.playing = true;
+	
+	    this.debug = true;
+	    this.useWorker = true;
+	
+	    this.webSocket = [];
+	    this.streamProtocol = "BVHStream";
+	    this.keepStreamedFrames = true;
+	    this.isStreaming = false;
+	
+	    var self = this;
+	
+	    //
+	
+	    this.log = function(m) {
+	        if (self.debug)
+	            console.log(self.name + ": " + m.toString());
+	    };
+	
+	    this.loadFromURL = function(url, callback) {
+	        self.log("Loading the mocap file ...");
+	        //Pace.start();
+	        reader = new parsers.bvhParser(this.name + "READER");
+	        this.url = url;
+	        reader.load(url, self.createSkel, self.fillFrames);
+	
+	        this.callb = callback;
+	    };
+	
+	    this.fillFrames = function() {
+	        // self.log("Ready!");
+	        self.ready = true;
+	        self.playing = true;
+	
+	        if (self.callb)
+	            self.callb();
+	    }
+	
+	    this.createSkel = function(data) {
+	        self.bvh = data;
+	        self.frameCount = data.frameCount;
+	        self.frameTime = data.frameTime;
+	
+	        self.log("Mocap file loaded.");
+	
+	        self.log("Creating the WebGL Joints.");
+	        self.buildSkelJoints(self.bvh.getSkeleton(), 0);
+	
+	        self.log("Creating the WebGL Bones.");
+	        self.buildSkelBones(self.jointMeshes[0]);
+	        
+	        self.skeleton.add(self.jointMeshes[0]);
+	        self.setSkeletonScale(self.skelScale);
+	        self.setSkelUp();
+	    };
+	
+	
+	    // Beginning of the Stream Code
+	    this.onHeaderReceived = function(data) {
+	        self.log("Loading the mocap header (skeleton) from the stream...");
+	        headerReader = new parsers.bvhStreamParser();
+	        headerReader.readHeader(data, self.createSkel);
+	
+	        if (self.callb)
+	            self.callb();
+	
+	        Pace.stop();
+	    }
+	
+	    this.onDataChunckReceived = function(rawFrames) {
+	        var aa = [];
+	
+	        for (f = 1; f < rawFrames.length; f++) {
+	            var parts = rawFrames[f].trim().split(" ");
+	            for (var j = 0; j < parts.length; j++)
+	                parts[j] = +parts[j];
+	            aa.push(parts);
+	        }
+	        diff = self.bvh.fillFrameArray(aa);
+	        self.frameCount = self.bvh.frameArray.length;
+	
+	
+	        if (!self.playing) {
+	            self.animStartTimeRef = Date.now();
+	            //  self.animOffset -= rawFrames.length;
+	        }
+	        /*
+	        // else
+	        // self.animOffset = self.animIndex;
+	        if (diff > 0)
+	        	self.animOffset -= rawFrames.length + 1;
+	        // self.animIndex -= rawFrames.length; //math.max(0,math.min(rawFrames.length, self.bvh.bufferSize));
+	        */
+	        self.fillFrames();
+	        Pace.stop();
+	    }
+	
+	    this.loadFromStream = function(url, callback) {
+	        self.log("Connecting to the stream server...");
+	        self.isStreaming = true;
+	        this.callb = callback;
+	        self.webSocket = new WebSocket(url);
+	
+	        self.webSocket.onerror = function(event) {
+	            self.log("Error connecting to the stream server " + event.origin);
+	        };
+	
+	        self.webSocket.onopen = function(event) {
+	            self.log("Connected to the stream server " + event.origin);
+	            Pace.stop();
+	        };
+	
+	        self.webSocket.onmessage = function(event) {
+	            // I'm not doing much of a type and content checking here. Let's just trust the sender for now!
+	            // Protocol for header:
+	            // $HEADER$
+	            // BVH...
+	            // Protocl for data chunk with id#:
+	            // $FRAMES$id#$
+	
+	            var messageLines = event.data.split('\n');
+	
+	            // self.log("Received somthing!");
+	            // self.log("The first line is : " + messageLines[0]);
+	
+	            if (messageLines.length < 1)
+	                return;
+	
+	            if (messageLines[0] == "$HEADER$") {
+	                self.onHeaderReceived(event.data);
+	
+	            } else if (messageLines[0].startsWith("$FRAMES$")) {
+	                chunckID = parseInt(messageLines[0].split("$")[2]);
+	                self.onDataChunckReceived(messageLines, chunckID);
+	            }
+	        };
+	
+	    };
+	
+	    this.requestFrames = function(i) {
+	        self.webSocket.send("$GETFRAMES" + i + "$");
+	    }
+	
+	    // End of the Stream Code
+	
+	    this.setOriginPosition = function(x, y, z) {
+	        self.originPosition.set(x, y, z);
+	    };
+	
+	    this.setSkeletonScale = function(s) {
+	        self.rootMeshes.forEach(function(c) {
+	            c.scale.set(s, s, s);
+	        });
+	        self.jointMeshes[0].scale.set(s, s, s);
+	        self.jointMeshes[0].position.multiplyScalar(s);
+	    };
+	
+	    this.buildSkelJoints = function(joint, parent) {
+	        var jointMesh = new THREE.Mesh(self.makeJointGeometryFCN(joint.name, self.skelScale), self.jointMaterial);
+	        jointMesh.bvhIndex = joint.jointIndex;
+	        jointMesh.offsetVec = new THREE.Vector3(joint.offset[0], joint.offset[1], joint.offset[2]);
+	        jointMesh.name = joint.name;
+	        jointMesh.jointparent = parent;
+	        var a, b, c;
+	        if (!joint.isEndSite()) {
+	            a = joint.channelNames[joint.channelNames.length - 3][0];
+	            b = joint.channelNames[joint.channelNames.length - 2][0];
+	            c = joint.channelNames[joint.channelNames.length - 1][0];
+	        }
+	        jointMesh.rotOrder = a + b + c;
+	        self.jointMeshes.push(jointMesh);
+	        
+	        jointMesh.position.set(jointMesh.offsetVec.x, jointMesh.offsetVec.y, jointMesh.offsetVec.z);
+	        
+	        // var axisHelper = new THREE.AxisHelper( 10 / self.skelScale );
+	        // jointMesh.add( axisHelper );
+	
+	
+	        joint.children.forEach(function(child) {
+	            jointMesh.add(self.buildSkelJoints(child, 1));
+	        });
+	
+	        return jointMesh;
+	    };
+	
+	    this.buildSkelBones = function(rootJointMesh) {
+	        rootJointMesh.traverse(function(childJointMesh) {            
+	            if (childJointMesh.parent !== null)
+	            {
+	                if (typeof childJointMesh.bvhIndex === "undefined")
+	                    return;
+	                // move origin (.translate)
+	                // rotate
+	                // translate (offset + position)
+	                h = math.abs(childJointMesh.offsetVec.length());
+	                var bgeometry = self.makeBoneGeometryFCN(childJointMesh.parent.name, childJointMesh.name, h, self.skelScale);
+	                
+	                //BEGIN - Universal      
+	                if (childJointMesh.offsetVec.y !== 0)                                               
+	                    // bgeometry.translate(0, Math.sign(childJointMesh.offsetVec.y) * h / 2, 0);
+	                    bgeometry.translate(0, -h/2, 0);
+	                else                                
+	                    bgeometry.translate(0, -h / 2,  0);
+	                    
+	                               
+	                dx = Math.atan2(childJointMesh.offsetVec.z,childJointMesh.offsetVec.y);                  
+	                dy = Math.atan2(childJointMesh.offsetVec.x,childJointMesh.offsetVec.z);
+	                dz = Math.atan2(childJointMesh.offsetVec.x,childJointMesh.offsetVec.y);   
+	   
+	   
+	                osx = math.sign(childJointMesh.offsetVec.x) === 0 ? 0: math.sign(childJointMesh.offsetVec.x);
+	                osy = math.sign(childJointMesh.offsetVec.y) === 0 ? 0: math.sign(childJointMesh.offsetVec.y);
+	                osz = math.sign(childJointMesh.offsetVec.z) === 0 ? 0: math.sign(childJointMesh.offsetVec.z);
+	
+	                osxy = math.sign(childJointMesh.offsetVec.x) === 0 ? 0: math.sign(childJointMesh.offsetVec.y);
+	                osyx = math.sign(childJointMesh.offsetVec.y) === 0 ? 0: math.sign(childJointMesh.offsetVec.x);
+	                osyz = math.sign(childJointMesh.offsetVec.y) === 0 ? 0: math.sign(childJointMesh.offsetVec.z);
+	                oszy = math.sign(childJointMesh.offsetVec.z) === 0 ? 0: math.sign(childJointMesh.offsetVec.y);
+	
+	                
+	                if (osz <0)
+	                    bgeometry.rotateZ(1*(math.pi-dz));
+	                else if (osz === 0)
+	                    bgeometry.rotateZ(1*(math.pi-dz));
+	                    // console.log();  
+	                else if (osz > 0)
+	                    bgeometry.rotateZ(1*(2*math.pi-dz));
+	
+	
+	                if (oszy >0)
+	                    bgeometry.rotateX(-1 *(2*math.pi-dx));
+	                else if (childJointMesh.offsetVec.z === 0)
+	                    // bgeometry.rotateX(-1*(math.pi-dx));
+	                    console.log();
+	                else if (oszy < 0)
+	                    bgeometry.rotateX(-1*(2*math.pi-dx));
+	                                                            
+	                //  bgeometry.rotateY(math.pi-dy);
+	                                                  
+	                //END - Universal
+	                
+	                var boneMesh = new THREE.Mesh(bgeometry, self.boneMaterial);                
+	                
+	                boneMesh.joint = childJointMesh.parent;
+	                boneMesh.name = childJointMesh.parent.name + " > " + childJointMesh.name;
+	                
+	                childJointMesh.parent.add(boneMesh);                               
+	                self.boneMeshes.push(boneMesh);                
+	            }
+	        });      
+	    };
+	
+	    this.animFrame = function(frame) {
+	        var torad = Math.PI / 180;
+	        
+	        if (frame >= self.frameCount) {
+	            self.playing = false;
+	            return;
+	        }
+	        
+	
+	        this.jointMeshes[0].traverse(function(joint) {
+	            
+	            if (typeof joint.bvhIndex === "undefined") {
+	                return;
+	            }
+	                
+	
+	            var bj = self.bvh.jointArray[joint.bvhIndex];
+	            var offsetVec = joint.offsetVec;
+	            
+	            var thisEuler = [];
+	
+	
+	            thisEuler = new THREE.Euler(
+	                (bj.channels[frame][bj.rotationIndex.x] * torad),
+	                (bj.channels[frame][bj.rotationIndex.y] * torad),
+	                (bj.channels[frame][bj.rotationIndex.z] * torad), joint.rotOrder);
+	
+	
+	            joint.localRotMat = new THREE.Matrix4();
+	            joint.localRotMat.makeRotationFromEuler(thisEuler);
+	            joint.rotation.setFromRotationMatrix(joint.localRotMat);
+	
+	            if (joint.jointparent !== 0) {
+	                // joint.position.set(offsetVec.x, offsetVec.y, offsetVec.z);
+	            } else { // root
+	                joint.position.set(
+	                    bj.channels[frame][bj.positionIndex.x] * self.skelScale + self.originPosition.x,
+	                    bj.channels[frame][bj.positionIndex.y] * self.skelScale + self.originPosition.y,
+	                    bj.channels[frame][bj.positionIndex.z] * self.skelScale + self.originPosition.z);
+	            }
+	        });
+	
+	        if (self.isStreaming) {
+	            self.bvh.consumeFrames(frame);
+	            self.frameCount = self.bvh.frameArray.length;
+	            // console.log(self.frameCount);
+	            if (self.frameCount <= 0)
+	                self.playing = false;
+	
+	            self.animOffset = 0; // self.animOffset - frame;
+	            self.animStartTimeRef = Date.now();
+	        }
+	    };
+	
+	    this.setSkelUp = function() {
+	        this.jointMeshes[0].traverse(function(joint) {
+	            if (typeof joint.bvhIndex === "undefined")
+	                return;
+	
+	            var bj = self.bvh.jointArray[joint.bvhIndex];
+	
+	            var offsetVec = joint.offsetVec;
+	            var torad = Math.PI / 180;
+	            var thisEuler = [];
+	
+	            thisEuler = new THREE.Euler(0, 0, 0, joint.rotOrder);
+	
+	            joint.localRotMat = new THREE.Matrix4();
+	            joint.localRotMat.makeRotationFromEuler(thisEuler);
+	            joint.rotation.setFromRotationMatrix(joint.localRotMat);
+	
+	            if (joint.jointparent !== 0) {
+	                // joint.position.set(offsetVec.x, offsetVec.y, offsetVec.z);
+	            } else { // root
+	                joint.position.set(self.originPosition.x, self.originPosition.y, self.originPosition.z);
+	            }
+	        });
+	    };
+	};
+	
+	
+	module.exports = BVHCharacter;
+
+/***/ },
+/* 2 */
+/***/ function(module, exports, __webpack_require__) {
+
+	module.exports ={
+	    bvhParser: __webpack_require__(3),
+	    bvhStreamParser: __webpack_require__(4)
+	};
+
+/***/ },
+/* 3 */
+/***/ function(module, exports) {
+
+	// By Ankit
+	var BVHReader = function () {
+	    this.load = function (url, callbackHeader, callbackFrameArray) {
+	        $.get(url, function (str) {
+	            
+	            var dataReturn = parse(str);
+	
+	            
+	            var jointStack = dataReturn[0];
+	            var jointMap = dataReturn[1];
+	            var jointArray = dataReturn[2];
+	            var connectivityMatrix = dataReturn[3]
+	            _bvh = new BVHReader.BVH.Skeleton(jointStack[0], jointMap, jointArray, dataReturn[3], dataReturn[4], dataReturn[5], []);
+	            
+	            if (callbackHeader)
+	                callbackHeader(_bvh,'BVH');
+	            console.log("Blah");
+	            _bvh.fillFrameArray(dataReturn[6]);
+	
+	            if (callbackFrameArray)
+	                   callbackFrameArray();
+	
+	        });
+	    };
+	
+	    function parse(str) {
+	        var lines = str.split('\n');
+	        var jointStack = [];
+	        var jointMap = {};
+	        var jointArray = [];
+	        var connectivityMatrix = [];
+	        var frameCount, frameTime, frameArray = [];
+	        var i = 0;
+	        //parse structure
+	        for (i = 1; i < lines.length; i++) {
+	            if (!parseLine(lines[i], jointStack, jointMap, jointArray, connectivityMatrix)) {
+	                break;
+	            }
+	        }
+	
+	        for (i = i + 1; i < lines.length; i++) {
+	            var line = lines[i].trim();
+	            //when encountering last line
+	            if (line === "")
+	                break;
+	            if (line.indexOf("Frames") === 0) {
+	                frameCount = +(line.split(/\b/)[2]);
+	            } else if (line.indexOf("Frame Time") === 0) {
+	                frameTime = +( line.substr(line.indexOf(":") + 1).trim() )
+	            } else {
+	                var parts = line.split(" ");
+	                for (var j = 0; j < parts.length; j++)
+	                    parts[j] = +parts[j];
+	                frameArray.push(parts);
+	            }
+	        }
+	
+	        //parse motion
+	        return [jointStack, jointMap, jointArray, connectivityMatrix, frameCount, frameTime, frameArray];
+	    }
+	
+	    //parses individual line in the bvh file.
+	    var parseLine = function (line, jointStack, jointMap, jointArray, connectivityMatrix) {
+	        line = line.trim();
+	        if (line.indexOf("ROOT") > -1 || line.indexOf("JOINT") > -1 || line.indexOf("End") > -1) {
+	            var parts = line.split(" ");
+	            var title = parts[1]; //temporary variable to be used after creating the joint object
+	            parts[1] = parts[1] + "-" + jointArray.length;
+	            var joint = new BVHReader.BVH.Joint(parts[1]);
+	            joint.title = title;
+	            jointStack.push(joint);
+	
+	            joint.jointIndex = Object.keys(jointMap).length;
+	            jointMap[parts[1]] = joint;
+	            jointArray.push(joint);
+	            //if the joint is not an end site
+	            if( line.indexOf("End") != 0 ){
+	                if (jointArray.length == 1) {
+	                    joint.channelOffset = 0;
+	                } else {
+	                    joint.channelOffset = jointArray[jointArray.length - 2].channelOffset + jointArray[jointArray.length - 2].channelLength;
+	                }
+	            }else{
+	                //channelLength is 0 for end joints
+	                joint.channelLength = 0;
+	                joint.channelOffset = jointArray[jointArray.length - 2].channelOffset + jointArray[jointArray.length - 2].channelLength;
+	            }
+	
+	        } else if (line.indexOf("{") === 0) {
+	
+	        } else if (line.indexOf("OFFSET") === 0) {
+	            var parts = line.split(" ");
+	            jointStack[jointStack.length - 1]["offset"] = parts.slice(1);
+	            for(x in jointStack[jointStack.length - 1]["offset"]){
+	                jointStack[jointStack.length - 1]["offset"][x] = +jointStack[jointStack.length - 1]["offset"][x]
+	            }
+	        } else if (line.indexOf("CHANNELS") === 0) {
+	            var parts = line.split(" ");
+	            jointStack[jointStack.length - 1].setChannelNames(parts.slice(2));
+	            jointStack[jointStack.length - 1]["channelLength"] = +parts[1];
+	        } else if (line.indexOf("}") === 0) {
+	            if (jointStack.length > 1) {
+	                child = jointStack.pop();
+	                jointStack[jointStack.length - 1].children.push(child);
+	                child.parent = jointStack[jointStack.length - 1];
+	
+	                connectivityMatrix.push([child.parent, child])
+	
+	                // if(!connectivityMatrix[child.name]){
+	                //     connectivityMatrix[child.name] = {}
+	                // }
+	                // connectivityMatrix[child.name][child.parent.name] = 1;
+	
+	                // if(!connectivityMatrix[child.parent.name]){
+	                //     connectivityMatrix[child.parent.name] = {}
+	                // }
+	                // connectivityMatrix[child.parent.name][child.name] = 1;
+	            }
+	        } else if (line.indexOf("MOTION") == 0) {
+	            return false;
+	        }
+	
+	        return true;
+	    };
+	};
+	
+	BVHReader.BVH = BVHReader.BVH || {};
+	
+	BVHReader.BVH.Joint = function (name, index) {
+	
+	    this.name = name;
+	    this.children = [];
+	    this.isEndSite = function () {
+	        return this.children.length == 0;
+	    };
+	    this.rotationIndex = {};
+	    this.positionIndex = {};
+	
+	    this.getChannels = function () {
+	        var allChannels = [];
+	        for (i = 0; i < this.skeleton.frameArray.length; i++) {
+	            allChannels.push(this.getChannelsAt(i));
+	        }
+	        return allChannels;
+	    };
+	    this.getChannelsAt = function (frameNum) {
+	        var channelsAtFrame = this.skeleton.frameArray[frameNum];
+	        return channelsAtFrame.slice(this.channelOffset, this.channelOffset + this.channelLength);
+	    };
+	
+	    this.setChannelNames = function (nameArr){
+	        this.channelNames = nameArr;
+	        for(i in this.channelNames){
+	            var name = this.channelNames[i];
+	            switch(name){
+	                case "Xposition": this.positionIndex.x = i; break;
+	                case "Yposition": this.positionIndex.y = i; break;
+	                case "Zposition": this.positionIndex.z = i; break;
+	
+	                case "Xrotation": this.rotationIndex.x = i; break;
+	                case "Yrotation": this.rotationIndex.y = i; break;
+	                case "Zrotation": this.rotationIndex.z = i; break;
+	            }
+	        }
+	    }
+	};
+	
+	BVHReader.BVH.Skeleton = function (root, map, arr, connectivityMatrix, frameCount, frameTime, frameArray) {
+	    thisSkeleton = this;
+	    this.root = root;
+	    this.jointMap = map;
+	    this.jointArray = arr;
+	    this.connectivityMatrix = connectivityMatrix;
+	    this.frameCount = frameCount;
+	    this.frameTime = frameTime;
+	    this.frameArray = frameArray;
+	
+	    for (i = 0; i < this.jointArray.length; i++) {
+	        this.jointArray[i].skeleton = thisSkeleton;
+	    }
+	
+	
+	
+	    this.fillFrameArray = function (fa) {
+	        this.frameArray = fa;
+	        this.frameCount = fa.length;
+	            //all the structures are ready. let's calculate the positions
+	    for(j=0; j < this.jointArray.length; j++){
+	        var joint = this.jointArray[j];
+	        updateWithPositions(joint);
+	    }
+	    }
+	
+	    this.getChannels = function () {
+	        return frameArray;
+	    };
+	    this.getChannelsAt = function (frameNum) {
+	    	//How do I know which column is what?
+	        //Why do you need the column index?
+	        return frameArray[frameNum];
+	    };
+	    this.getFrameRate = function () {
+	        return frameCount / frameTime;
+	    };
+	    this.getSkeleton = function () {
+	        return root;
+	    };
+	
+	    this.getHeadJoint = function () {
+	    	// do a quick search in the joint names to see if any of them matches head, else return the something!!!!
+	        return jointMap["Head"];
+	    };
+	    this.getPositionsAt = function (frameNum) {
+	    	//for each joint, calculate its position in XYZ
+	        //return an array of joints, each with .x, .y, and .z properties
+	    	posFrame = [];
+	
+	    	for (j=0;j<this.jointArray.length;j++) {
+	    		posFrame.push(this.jointArray[j].positions[frameNum]);
+	    	}
+	
+	    	posFrame = posFrame.map(function(d) {
+				return {
+					x : d[0],
+					y : d[1],
+					z : d[2],
+				};
+			});
+	
+	        return posFrame;
+	    };
+	    this.getTPose = function () {
+	    	// This function is basically the same as the getPositionsAt except that all the rotations will be 0
+	        console.log("Not yet implemented");
+	    };
+	
+	    function updatePositions(rootOffset, removeRoot, orientation, camera) {
+	      //TODO: compelte the specification of this
+	
+	      for(j=0; j < this.jointArray.length; j++){
+	          var joint = this.jointArray[j];
+	          updateWithPositions(joint);
+	      }
+	    }
+	
+	    function updateWithPositions(joint){
+	        var channelNames = joint.channelNames;
+	        joint.channels = joint.getChannels();
+	        joint.rotations = [];
+	        joint.positions = [];
+	        joint.rotmat = [];
+	        for(i in joint.channels){
+	            var channel = joint.channels[i];
+	            var xpos = channel[joint.positionIndex.x] || 0,
+	            ypos =  channel[joint.positionIndex.y] || 0,
+	            zpos =  channel[joint.positionIndex.z] || 0,
+	            xangle =  deg2rad(channel[joint.rotationIndex.x] || 0),
+	            yangle =  deg2rad(channel[joint.rotationIndex.y] || 0),
+	            zangle= deg2rad(channel[joint.rotationIndex.z] || 0);
+	
+	            // var rotMatrix = math.transpose(getRotationMatrix(xangle, yangle, zangle, "xyz"));
+	            // var rotMatrix = getRotationMatrix1(xangle, yangle, zangle, "xyz"); //this also works
+	            var posMatrix = [xpos, ypos, zpos];
+	
+	            if(joint.parent){
+	            	  posMatrix = [0,0,0];  //At least for the bvhs that we have, this should be set to 0
+	
+	                // var t = vectorAdd(joint.offset, posMatrix);
+	                // var u = matrixMultiply(t, joint.parent.rotations[i]);
+	
+	                // joint.positions[i] = vectorAdd(u, joint.parent.positions[i]);
+	                // joint.rotations[i] = matrixMultiply( rotMatrix, joint.parent.rotations[i]);
+	                // joint.rotmat[i] = rotMatrix; 
+	
+	                 if (i==0 && (joint.name ==  "Spine" || joint.name == "L_Femur")) {
+	                    /*console.log("head's rot mat: ");
+	                    console.log(joint.rotations[i]);
+	                    console.log(t);
+	                    console.log(u);
+	
+	                    console.log("x: "+xangle + "y: "+yangle + "z: "+zangle );
+	                    console.log(posMatrix);
+	                    */
+	                }
+	
+	            }else{
+	                //its the root
+	                // joint.rotations[i] = rotMatrix;
+	                // joint.rotmat[i] = rotMatrix;
+	                joint.positions[i] = posMatrix;//vectorAdd(joint.offset , posMatrix);
+	                // ^ we can safely ignore the root's offset
+	            }
+	        }
+	    }
+	
+	    function deg2rad(deg){
+	        return deg * (Math.PI/180);
+	    }
+	
+	
+	    function getRotationMatrix(alpha, beta, gamma) {
+	
+	    //inputs are the intrinsic rotation angles in RADIANTS
+	    var ca = Math.cos(alpha),
+	    	sa = Math.sin(alpha),
+	
+	    	cb = Math.cos(beta),
+	    	sb = Math.sin(beta),
+	
+	    	cg = Math.cos(gamma),
+	    	sg = Math.sin(gamma),
+	
+	    Rx = [[1, 0, 0], [0, ca, -sa], [0, sa, ca]];
+	
+	    Ry = [[cb, 0, sb], [0, 1, 0], [-sb, 0, cb]];
+	
+	    Rz = [[cg, -sg, 0], [sg, cg, 0], [0,    0,   1]];
+	
+	
+	
+	
+	    var Rzm = math.matrix(Rz);
+	    var Rym = math.matrix(Ry);
+	    var Rxm = math.matrix(Rx);
+	
+	    var tt = math.multiply(Rzm, Rym);
+	
+	    return  math.multiply(tt,Rxm).toArray();
+	    //rotationMatrix = math. //Rz*Ry*Rx;
+	
+	    //     R = Rx*Ry*Rz;
+		}
+	
+	    function getRotationMatrix1 (xangle, yangle, zangle, order){
+	        var c1 = Math.cos(xangle),
+	        c2 = Math.cos(yangle),
+	        c3 = Math.cos(zangle),
+	        s1 = Math.sin(xangle),
+	        s2 = Math.sin(yangle),
+	        s3 = Math.sin(zangle);
+	
+	        if(order === undefined || order.trim() === ""){
+	            order = "zxy";
+	        }
+	
+	        var rotMat = [
+	            [1,0,0],
+	            [0,1,0],
+	            [0,0,1]
+	        ];
+	
+	        switch(order){
+	            case "___zxy":
+	                rotMat = [
+	                    [c2*c3-s1*s2*s3, c2*s3+s1*s2*c3, -s2*c1],
+	                    [-c1*s3, c1*c3, s1],
+	                    [s2*c3+c2*s1*s3, s2*s3-c2*s1*c3, c2*c1]
+	                ];
+	            break;
+	            default:
+	              for (o in order){
+	                var axis = order[o];
+	                var t;
+	                switch(axis){
+	                    case "x":
+	                        t = [
+	                                [1, 0, 0],
+	                                [0,  c1, s1],
+	                                [0, -s1, c1],
+	                            ]
+	                        break;
+	                    case "y":
+	                        t = [
+	                                [c2,0,-s2],
+	                                [0,1,0],
+	                                [s2,0,c2]
+	                            ]
+	                        break;
+	                    case "z":
+	                        t = [[c3,s3,0],[-s3,c3,0],[0,0,1]]
+	                        break;
+	                }
+	
+	                rotMat = matrixMultiply(t, rotMat)
+	              }
+	          }
+	
+	        return rotMat;
+	    }
+	};
+	
+	function vectorAdd(a, b){
+	    return math.add(math.matrix(a), math.matrix(b)).toArray();
+	}
+	
+	function matrixMultiply(m1, m2) {
+	    var a = math.matrix(m1);
+	    var b = math.matrix(m2);
+	    return math.multiply(a, b).toArray();
+	}
+	
+	
+	module.exports = BVHReader;
+
+/***/ },
+/* 4 */
+/***/ function(module, exports) {
+
+	// BVH parser by Ankit
+	// Stream by Omid
+	
+	
+	var BVHStreamParser = function () {
+	    this.readHeader = function (str, callback) {
+	            var dataReturn = parseHeader(str);
+	            var jointStack = dataReturn[0];
+	            var jointMap = dataReturn[1];
+	            var jointArray = dataReturn[2];
+	            var connectivityMatrix = dataReturn[3]
+	            if (callback)
+	                callback(new BVHStreamParser.BVH.Skeleton(jointStack[0], jointMap, jointArray, dataReturn[3], 0, dataReturn[5], dataReturn[6]),'BVH');
+	    };
+	
+	    function parseHeader(str) {
+	        var lines = str.split('\n');
+	        var jointStack = [];
+	        var jointMap = {};
+	        var jointArray = [];
+	        var connectivityMatrix = [];
+	        var frameCount, frameTime, frameArray = [];
+	        var i = 0;
+	        //parse structure
+	        for (i = 2; i < lines.length; i++) { //  start from 2 to skip the $HEADER$ command
+	            if (!parseLine(lines[i], jointStack, jointMap, jointArray, connectivityMatrix)) {
+	                break;
+	            }
+	        }
+	
+	        for (i = i + 1; i < lines.length; i++) {
+	            var line = lines[i].trim();
+	            //when encountering last line
+	            if (line === "")
+	                break;
+	            if (line.indexOf("Frames") === 0) {
+	                frameCount = +(line.split(/\b/)[2]);
+	            } else if (line.indexOf("Frame Time") === 0) {
+	                frameTime = +( line.substr(line.indexOf(":") + 1).trim() )
+	            } else { /// maybe this should be removed
+	                var parts = line.split(" ");
+	                for (var j = 0; j < parts.length; j++)
+	                    parts[j] = +parts[j];
+	                frameArray.push(parts);
+	            }
+	        }
+	
+	        //parse motion
+	        return [jointStack, jointMap, jointArray, connectivityMatrix, frameCount, frameTime, frameArray];
+	    }
+	
+	    //parses individual line in the bvh file.
+	    var parseLine = function (line, jointStack, jointMap, jointArray, connectivityMatrix) {
+	        line = line.trim();
+	        if (line.indexOf("ROOT") > -1 || line.indexOf("JOINT") > -1 || line.indexOf("End") > -1) {
+	            var parts = line.split(" ");
+	            var title = parts[1]; //temporary variable to be used after creating the joint object
+	            parts[1] = parts[1] + "-" + jointArray.length;
+	            var joint = new BVHStreamParser.BVH.Joint(parts[1]);
+	            joint.title = title;
+	            jointStack.push(joint);
+	
+	            joint.jointIndex = Object.keys(jointMap).length;
+	            jointMap[parts[1]] = joint;
+	            jointArray.push(joint);
+	            //if the joint is not an end site
+	            if( line.indexOf("End") != 0 ){
+	                if (jointArray.length == 1) {
+	                    joint.channelOffset = 0;
+	                } else {
+	                    joint.channelOffset = jointArray[jointArray.length - 2].channelOffset + jointArray[jointArray.length - 2].channelLength;
+	                }
+	            }else{
+	                //channelLength is 0 for end joints
+	                joint.channelLength = 0;
+	                joint.channelOffset = jointArray[jointArray.length - 2].channelOffset + jointArray[jointArray.length - 2].channelLength;
+	            }
+	
+	        } else if (line.indexOf("{") === 0) {
+	
+	        } else if (line.indexOf("OFFSET") === 0) {
+	            var parts = line.split(" ");
+	            jointStack[jointStack.length - 1]["offset"] = parts.slice(1);
+	            for(x in jointStack[jointStack.length - 1]["offset"]){
+	                jointStack[jointStack.length - 1]["offset"][x] = +jointStack[jointStack.length - 1]["offset"][x]
+	            }
+	        } else if (line.indexOf("CHANNELS") === 0) {
+	            var parts = line.split(" ");
+	            jointStack[jointStack.length - 1].setChannelNames(parts.slice(2));
+	            jointStack[jointStack.length - 1]["channelLength"] = +parts[1];
+	        } else if (line.indexOf("}") === 0) {
+	            if (jointStack.length > 1) {
+	                child = jointStack.pop();
+	                jointStack[jointStack.length - 1].children.push(child);
+	                child.parent = jointStack[jointStack.length - 1];
+	
+	                connectivityMatrix.push([child.parent, child])
+	            }
+	        } else if (line.indexOf("MOTION") == 0) {
+	            return false;
+	        }
+	
+	        return true;
+	    };
+	};
+	
+	BVHStreamParser.BVH = BVHStreamParser.BVH || {};
+	
+	BVHStreamParser.BVH.Joint = function (name, index) {
+	
+	    this.name = name;
+	    this.children = [];
+	    this.isEndSite = function () {
+	        return this.children.length == 0;
+	    };
+	    this.rotationIndex = {};
+	    this.positionIndex = {};
+	
+	    this.getChannels = function () {
+	        var allChannels = [];
+	        for (i = 0; i < this.skeleton.frameArray.length; i++) {
+	            allChannels.push(this.getChannelsAt(i));
+	        }
+	        return allChannels;
+	    };
+	    this.getChannelsAt = function (frameNum) {
+	        var channelsAtFrame = this.skeleton.frameArray[frameNum];
+	        return channelsAtFrame.slice(this.channelOffset, this.channelOffset + this.channelLength);
+	    };
+	
+	    this.setChannelNames = function (nameArr){
+	        this.channelNames = nameArr;
+	        for(i in this.channelNames){
+	            var name = this.channelNames[i];
+	            switch(name){
+	                case "Xposition": this.positionIndex.x = i; break;
+	                case "Yposition": this.positionIndex.y = i; break;
+	                case "Zposition": this.positionIndex.z = i; break;
+	
+	                case "Xrotation": this.rotationIndex.x = i; break;
+	                case "Yrotation": this.rotationIndex.y = i; break;
+	                case "Zrotation": this.rotationIndex.z = i; break;
+	            }
+	        }
+	    }
+	};
+	
+	BVHStreamParser.BVH.Skeleton = function (root, map, arr, connectivityMatrix, frameCount, frameTime, frameArray) {
+	    thisSkeleton = this;
+	    this.root = root;
+	    this.jointMap = map;
+	    this.jointArray = arr;
+	    this.connectivityMatrix = connectivityMatrix;
+	    this.frameCount = frameCount;
+	    this.frameTime = frameTime;
+	    this.frameArray = frameArray;
+	    this.bufferSize = 500;
+	
+	    for (i = 0; i < this.jointArray.length; i++) {
+	        this.jointArray[i].skeleton = thisSkeleton;
+	    }
+	
+	    this.fillFrameArray = function (fa) {
+	        this.frameArray.push.apply(this.frameArray,fa);
+	        //this.frameArray.push.apply(this.frameArray,fa);
+	        
+	        diff = this.frameArray.length - this.bufferSize;
+	        // console.log('diff = ' + diff);
+	        
+	        /*
+	        if (diff > 0) 
+	            for (i=0;i<diff;i++)
+	                this.frameArray.shift();
+	
+	        this.frameCount = this.frameArray.length;
+	        */
+	         
+	        if (diff > 0) 
+	            addedCount = this.frameCount;
+	        else
+	            addedCount = fa.length;
+	
+	        for(j=0; j < this.jointArray.length; j++){
+	            var joint = this.jointArray[j];
+	            updateWithPositionsSinceLast(joint, addedCount);
+	        }
+	        
+	        return diff;
+	    }
+	
+	    this.consumeFrames = function (index) {
+	        for (i=0;i<=index;i++) {
+	            this.frameArray.shift();
+	            for (j=0;j<this.jointArray.length;j++)
+	                this.jointArray[j].channels.shift();
+	        }
+	        this.frameCount = this.frameArray.length;
+	    }
+	
+	    this.getChannels = function () {
+	        return frameArray;
+	    };
+	    this.getChannelsAt = function (frameNum) {
+	    	//How do I know which column is what?
+	        //Why do you need the column index?
+	        return frameArray[frameNum];
+	    };
+	    this.getFrameRate = function () {
+	        return frameCount / frameTime;
+	    };
+	    this.getSkeleton = function () {
+	        return root;
+	    };
+	
+	    this.getHeadJoint = function () {
+	    	// do a quick search in the joint names to see if any of them matches head, else return the something!!!!
+	        return jointMap["Head"];
+	    };
+	    this.getPositionsAt = function (frameNum) {
+	    	//for each joint, calculate its position in XYZ
+	        //return an array of joints, each with .x, .y, and .z properties
+	    	posFrame = [];
+	
+	    	for (j=0;j<this.jointArray.length;j++) {
+	    		posFrame.push(this.jointArray[j].positions[frameNum]);
+	    	}
+	
+	    	posFrame = posFrame.map(function(d) {
+				return {
+					x : d[0],
+					y : d[1],
+					z : d[2],
+				};
+			});
+	
+	        return posFrame;
+	    };
+	    this.getTPose = function () {
+	    	// This function is basically the same as the getPositionsAt except that all the rotations will be 0
+	        console.log("Not yet implemented");
+	    };
+	
+	    function updatePositions(rootOffset, removeRoot, orientation, camera) {
+	      //TODO: compelte the specification of this
+	
+	      for(j=0; j < this.jointArray.length; j++){
+	          var joint = this.jointArray[j];
+	          updateWithPositions(joint);
+	      }
+	    }
+	
+	    function updateWithPositions(joint){
+	        var channelNames = joint.channelNames;
+	        joint.channels = joint.getChannels();
+	        joint.rotations = [];
+	        joint.positions = [];
+	        joint.rotmat = [];
+	        for(i in joint.channels){
+	            var channel = joint.channels[i];
+	            var xpos = channel[joint.positionIndex.x] || 0,
+	            ypos =  channel[joint.positionIndex.y] || 0,
+	            zpos =  channel[joint.positionIndex.z] || 0;
+	            // xangle =  deg2rad(channel[joint.rotationIndex.x] || 0),
+	            // yangle =  deg2rad(channel[joint.rotationIndex.y] || 0),
+	            // zangle= deg2rad(channel[joint.rotationIndex.z] || 0);
+	
+	            var posMatrix = [xpos, ypos, zpos];
+	
+	            if(!joint.parent){
+	                //its the root
+	                joint.positions[i] = posMatrix;//vectorAdd(joint.offset , posMatrix);
+	                // ^ we can safely ignore the root's offset
+	            }
+	        }
+	    }
+	
+	    function updateWithPositionsSinceLast(joint, addedCount){
+	        var channelNames = joint.channelNames;
+	        joint.channels = joint.getChannels();
+	        joint.rotations = [];
+	        joint.positions = [];
+	        joint.rotmat = [];
+	        for(i=joint.channels.length - addedCount;i < joint.channels.length; i++){
+	            var channel = joint.channels[i];
+	            var xpos = channel[joint.positionIndex.x] || 0,
+	            ypos =  channel[joint.positionIndex.y] || 0,
+	            zpos =  channel[joint.positionIndex.z] || 0;
+	            // xangle =  deg2rad(channel[joint.rotationIndex.x] || 0),
+	            // yangle =  deg2rad(channel[joint.rotationIndex.y] || 0),
+	            // zangle= deg2rad(channel[joint.rotationIndex.z] || 0);
+	
+	            var posMatrix = [xpos, ypos, zpos];
+	
+	            if(!joint.parent){
+	                //its the root
+	                joint.positions[i] = posMatrix;//vectorAdd(joint.offset , posMatrix);
+	                // ^ we can safely ignore the root's offset
+	            }
+	        }
+	    }
+	
+	    function deg2rad(deg){
+	        return deg * (Math.PI/180);
+	    }
+	};
+	
+	module.exports = BVHStreamParser;
+
+/***/ },
+/* 5 */
+/***/ function(module, exports) {
+
+	var C3DCharacter = C3DCharacter || {};
+	
+	C3DCharacter = function(n, jm, jg){
+		this.name = n;
+		
+		this.markerMaterial = jm;
+		this.makeMarkerGeometryFCN = jg;
+	
+		this.originPosition = new THREE.Vector3(0,0,0);
+	
+		this.markerdata = [];
+		this.ready = false;
+		this.scale = 0.5;
+		this.markerMeshes = [];
+	
+		this.frameTime = 1/30;
+		this.frameCount = 0;
+	
+		this.animIndex = 0;
+		this.animStartTimeRef = 0;
+		this.animOffset = 0;
+		this.playing = true;
+	
+		this.debug = true;
+	
+		var self = this;
+	
+		//
+	
+		this.log = function(m) {
+			if (self.debug)
+				console.log(self.name + ": "+m.toString());
+		};
+	
+		this.loadFromURL = function(url, callback) {
+			self.log("Loading the mocap file ...");
+			Pace.start();
+			url2 = "../" + url;	
+			self.url = url;
+
+			Papa.parse(url2, {
+				worker: true,
+				delimiter: ",",	
+				dynamicTyping: true,
+				download: true,
+				header: false,
+				complete: function(results) {
+					self.processData(results);
+					if (callback)
+						callback();
+				}
+			});
+		};
+
+		this.loadFromBuffer = function(data, callback) {
+			self.log("Loading the mocap from buffer...");
+			Pace.start();	
+			var preData = data.split('\n');
+			preData = preData.map(function(d,i){
+				var cols = d.split(',');
+				var floats = cols;
+				// console.log(i);
+				if (i!=0) {
+					floats = cols.map(function(p, j){
+						return parseFloat(p);
+					});
+				}
+				
+				return floats;
+			});
+			preData.pop();			
+			
+			this.processData({data: preData});
+			if (callback)
+				callback();
+		}
+	
+		this.processData = function(results) {
+			//self.markerdata = results.data;
+			// console.log(results);
+
+			for (i=0;i<results.data[0].length-3;i+=3) {
+				var markerMesh = new THREE.Mesh(self.makeMarkerGeometryFCN(results.data[0][i], self.scale), self.markerMaterial);
+				markerMesh.markerIndex = i;
+				markerMesh.name = results.data[0][i];
+				scene.add(markerMesh);
+				self.markerMeshes.push(markerMesh);				
+			}	
+				
+			self.markerNames = results.data[0];
+
+			for (f=1;f<results.data.length;f++) {
+				self.markerdata[f-1] = [];
+				for (m=0;m<results.data[f].length-3;m+=3) {
+					marker = {};
+					marker.x = results.data[f][m];
+					marker.y = results.data[f][m+1];
+					marker.z = results.data[f][m+2];
+					marker.name = self.markerNames[m];
+
+					self.markerdata[f-1].push(marker);
+				}								
+			}						
+
+			self.frameCount = self.markerdata.length;
+			self.log("Done parsing!");	
+			self.ready = true;
+		}
+
+		this.setOriginPosition = function (x, y, z) {
+			self.originPosition.set(x,y,z);
+		};
+	
+		this.setSkeletonScale = function(s) {
+			self.rootMeshes.forEach(function (c) {
+				c.scale.set(s,s,s);
+			});
+			self.jointMeshes[0].scale.set(s,s,s);
+			self.jointMeshes[0].position.multiplyScalar(s);
+		};
+	
+	
+		this.animFrame = function (frame) {
+			for (m=0;m<self.markerMeshes.length; m++) {
+				self.markerMeshes[m].position.set(
+					self.markerdata[frame][m].x * self.scale + self.originPosition.x,
+					self.markerdata[frame][m].y * self.scale + self.originPosition.y,
+					self.markerdata[frame][m].z * self.scale + self.originPosition.z);
+			}
+		};
+	};
+	
+	module.exports = C3DCharacter;
+
+/***/ }
+/******/ ]);
+//# sourceMappingURL=mocapjs.js.map

dataloaders/pymo/mocapplayer/libs/pace.min.js

@@ -0,0 +1,2 @@
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dataloaders/pymo/mocapplayer/libs/papaparse.min.js

@@ -0,0 +1,6 @@
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dataloaders/pymo/mocapplayer/libs/threejs/Detector.js

@@ -0,0 +1,78 @@
+/**
+ * @author alteredq / http://alteredqualia.com/
+ * @author mr.doob / http://mrdoob.com/
+ */
+
+var Detector = {
+
+	canvas: !! window.CanvasRenderingContext2D,
+	webgl: ( function () {
+
+		try {
+
+			var canvas = document.createElement( 'canvas' ); return !! ( window.WebGLRenderingContext && ( canvas.getContext( 'webgl' ) || canvas.getContext( 'experimental-webgl' ) ) );
+
+		} catch ( e ) {
+
+			return false;
+
+		}
+
+	} )(),
+	workers: !! window.Worker,
+	fileapi: window.File && window.FileReader && window.FileList && window.Blob,
+
+	getWebGLErrorMessage: function () {
+
+		var element = document.createElement( 'div' );
+		element.id = 'webgl-error-message';
+		element.style.fontFamily = 'monospace';
+		element.style.fontSize = '13px';
+		element.style.fontWeight = 'normal';
+		element.style.textAlign = 'center';
+		element.style.background = '#fff';
+		element.style.color = '#000';
+		element.style.padding = '1.5em';
+		element.style.width = '400px';
+		element.style.margin = '5em auto 0';
+
+		if ( ! this.webgl ) {
+
+			element.innerHTML = window.WebGLRenderingContext ? [
+				'Your graphics card does not seem to support <a href="http://khronos.org/webgl/wiki/Getting_a_WebGL_Implementation" style="color:#000">WebGL</a>.<br />',
+				'Find out how to get it <a href="http://get.webgl.org/" style="color:#000">here</a>.'
+			].join( '\n' ) : [
+				'Your browser does not seem to support <a href="http://khronos.org/webgl/wiki/Getting_a_WebGL_Implementation" style="color:#000">WebGL</a>.<br/>',
+				'Find out how to get it <a href="http://get.webgl.org/" style="color:#000">here</a>.'
+			].join( '\n' );
+
+		}
+
+		return element;
+
+	},
+
+	addGetWebGLMessage: function ( parameters ) {
+
+		var parent, id, element;
+
+		parameters = parameters || {};
+
+		parent = parameters.parent !== undefined ? parameters.parent : document.body;
+		id = parameters.id !== undefined ? parameters.id : 'oldie';
+
+		element = Detector.getWebGLErrorMessage();
+		element.id = id;
+
+		parent.appendChild( element );
+
+	}
+
+};
+
+// browserify support
+if ( typeof module === 'object' ) {
+
+	module.exports = Detector;
+
+}

dataloaders/pymo/mocapplayer/libs/threejs/OrbitControls.js

@@ -0,0 +1,1037 @@
+/**
+ * @author qiao / https://github.com/qiao
+ * @author mrdoob / http://mrdoob.com
+ * @author alteredq / http://alteredqualia.com/
+ * @author WestLangley / http://github.com/WestLangley
+ * @author erich666 / http://erichaines.com
+ */
+
+// This set of controls performs orbiting, dollying (zooming), and panning.
+// Unlike TrackballControls, it maintains the "up" direction object.up (+Y by default).
+//
+//    Orbit - left mouse / touch: one finger move
+//    Zoom - middle mouse, or mousewheel / touch: two finger spread or squish
+//    Pan - right mouse, or arrow keys / touch: three finter swipe
+
+THREE.OrbitControls = function ( object, domElement ) {
+
+	this.object = object;
+
+	this.domElement = ( domElement !== undefined ) ? domElement : document;
+
+	// Set to false to disable this control
+	this.enabled = true;
+
+	// "target" sets the location of focus, where the object orbits around
+	this.target = new THREE.Vector3();
+
+	// How far you can dolly in and out ( PerspectiveCamera only )
+	this.minDistance = 0;
+	this.maxDistance = Infinity;
+
+	// How far you can zoom in and out ( OrthographicCamera only )
+	this.minZoom = 0;
+	this.maxZoom = Infinity;
+
+	// How far you can orbit vertically, upper and lower limits.
+	// Range is 0 to Math.PI radians.
+	this.minPolarAngle = 0; // radians
+	this.maxPolarAngle = Math.PI; // radians
+
+	// How far you can orbit horizontally, upper and lower limits.
+	// If set, must be a sub-interval of the interval [ - Math.PI, Math.PI ].
+	this.minAzimuthAngle = - Infinity; // radians
+	this.maxAzimuthAngle = Infinity; // radians
+
+	// Set to true to enable damping (inertia)
+	// If damping is enabled, you must call controls.update() in your animation loop
+	this.enableDamping = false;
+	this.dampingFactor = 0.25;
+
+	// This option actually enables dollying in and out; left as "zoom" for backwards compatibility.
+	// Set to false to disable zooming
+	this.enableZoom = true;
+	this.zoomSpeed = 1.0;
+
+	// Set to false to disable rotating
+	this.enableRotate = true;
+	this.rotateSpeed = 1.0;
+
+	// Set to false to disable panning
+	this.enablePan = true;
+	this.keyPanSpeed = 7.0;	// pixels moved per arrow key push
+
+	// Set to true to automatically rotate around the target
+	// If auto-rotate is enabled, you must call controls.update() in your animation loop
+	this.autoRotate = false;
+	this.autoRotateSpeed = 2.0; // 30 seconds per round when fps is 60
+
+	// Set to false to disable use of the keys
+	this.enableKeys = true;
+
+	// The four arrow keys
+	this.keys = { LEFT: 37, UP: 38, RIGHT: 39, BOTTOM: 40 };
+
+	// Mouse buttons
+	this.mouseButtons = { ORBIT: THREE.MOUSE.LEFT, ZOOM: THREE.MOUSE.MIDDLE, PAN: THREE.MOUSE.RIGHT };
+
+	// for reset
+	this.target0 = this.target.clone();
+	this.position0 = this.object.position.clone();
+	this.zoom0 = this.object.zoom;
+
+	//
+	// public methods
+	//
+
+	this.getPolarAngle = function () {
+
+		return spherical.phi;
+
+	};
+
+	this.getAzimuthalAngle = function () {
+
+		return spherical.theta;
+
+	};
+
+	this.reset = function () {
+
+		scope.target.copy( scope.target0 );
+		scope.object.position.copy( scope.position0 );
+		scope.object.zoom = scope.zoom0;
+
+		scope.object.updateProjectionMatrix();
+		scope.dispatchEvent( changeEvent );
+
+		scope.update();
+
+		state = STATE.NONE;
+
+	};
+
+	// this method is exposed, but perhaps it would be better if we can make it private...
+	this.update = function() {
+
+		var offset = new THREE.Vector3();
+
+		// so camera.up is the orbit axis
+		var quat = new THREE.Quaternion().setFromUnitVectors( object.up, new THREE.Vector3( 0, 1, 0 ) );
+		var quatInverse = quat.clone().inverse();
+
+		var lastPosition = new THREE.Vector3();
+		var lastQuaternion = new THREE.Quaternion();
+
+		return function () {
+
+			var position = scope.object.position;
+
+			offset.copy( position ).sub( scope.target );
+
+			// rotate offset to "y-axis-is-up" space
+			offset.applyQuaternion( quat );
+
+			// angle from z-axis around y-axis
+			spherical.setFromVector3( offset );
+
+			if ( scope.autoRotate && state === STATE.NONE ) {
+
+				rotateLeft( getAutoRotationAngle() );
+
+			}
+
+			spherical.theta += sphericalDelta.theta;
+			spherical.phi += sphericalDelta.phi;
+
+			// restrict theta to be between desired limits
+			spherical.theta = Math.max( scope.minAzimuthAngle, Math.min( scope.maxAzimuthAngle, spherical.theta ) );
+
+			// restrict phi to be between desired limits
+			spherical.phi = Math.max( scope.minPolarAngle, Math.min( scope.maxPolarAngle, spherical.phi ) );
+
+			spherical.makeSafe();
+
+
+			spherical.radius *= scale;
+
+			// restrict radius to be between desired limits
+			spherical.radius = Math.max( scope.minDistance, Math.min( scope.maxDistance, spherical.radius ) );
+
+			// move target to panned location
+			scope.target.add( panOffset );
+
+			offset.setFromSpherical( spherical );
+
+			// rotate offset back to "camera-up-vector-is-up" space
+			offset.applyQuaternion( quatInverse );
+
+			position.copy( scope.target ).add( offset );
+
+			scope.object.lookAt( scope.target );
+
+			if ( scope.enableDamping === true ) {
+
+				sphericalDelta.theta *= ( 1 - scope.dampingFactor );
+				sphericalDelta.phi *= ( 1 - scope.dampingFactor );
+
+			} else {
+
+				sphericalDelta.set( 0, 0, 0 );
+
+			}
+
+			scale = 1;
+			panOffset.set( 0, 0, 0 );
+
+			// update condition is:
+			// min(camera displacement, camera rotation in radians)^2 > EPS
+			// using small-angle approximation cos(x/2) = 1 - x^2 / 8
+
+			if ( zoomChanged ||
+				lastPosition.distanceToSquared( scope.object.position ) > EPS ||
+				8 * ( 1 - lastQuaternion.dot( scope.object.quaternion ) ) > EPS ) {
+
+				scope.dispatchEvent( changeEvent );
+
+				lastPosition.copy( scope.object.position );
+				lastQuaternion.copy( scope.object.quaternion );
+				zoomChanged = false;
+
+				return true;
+
+			}
+
+			return false;
+
+		};
+
+	}();
+
+	this.dispose = function() {
+
+		scope.domElement.removeEventListener( 'contextmenu', onContextMenu, false );
+		scope.domElement.removeEventListener( 'mousedown', onMouseDown, false );
+		scope.domElement.removeEventListener( 'mousewheel', onMouseWheel, false );
+		scope.domElement.removeEventListener( 'MozMousePixelScroll', onMouseWheel, false ); // firefox
+
+		scope.domElement.removeEventListener( 'touchstart', onTouchStart, false );
+		scope.domElement.removeEventListener( 'touchend', onTouchEnd, false );
+		scope.domElement.removeEventListener( 'touchmove', onTouchMove, false );
+
+		document.removeEventListener( 'mousemove', onMouseMove, false );
+		document.removeEventListener( 'mouseup', onMouseUp, false );
+		document.removeEventListener( 'mouseout', onMouseUp, false );
+
+		window.removeEventListener( 'keydown', onKeyDown, false );
+
+		//scope.dispatchEvent( { type: 'dispose' } ); // should this be added here?
+
+	};
+
+	//
+	// internals
+	//
+
+	var scope = this;
+
+	var changeEvent = { type: 'change' };
+	var startEvent = { type: 'start' };
+	var endEvent = { type: 'end' };
+
+	var STATE = { NONE : - 1, ROTATE : 0, DOLLY : 1, PAN : 2, TOUCH_ROTATE : 3, TOUCH_DOLLY : 4, TOUCH_PAN : 5 };
+
+	var state = STATE.NONE;
+
+	var EPS = 0.000001;
+
+	// current position in spherical coordinates
+	var spherical = new THREE.Spherical();
+	var sphericalDelta = new THREE.Spherical();
+
+	var scale = 1;
+	var panOffset = new THREE.Vector3();
+	var zoomChanged = false;
+
+	var rotateStart = new THREE.Vector2();
+	var rotateEnd = new THREE.Vector2();
+	var rotateDelta = new THREE.Vector2();
+
+	var panStart = new THREE.Vector2();
+	var panEnd = new THREE.Vector2();
+	var panDelta = new THREE.Vector2();
+
+	var dollyStart = new THREE.Vector2();
+	var dollyEnd = new THREE.Vector2();
+	var dollyDelta = new THREE.Vector2();
+
+	function getAutoRotationAngle() {
+
+		return 2 * Math.PI / 60 / 60 * scope.autoRotateSpeed;
+
+	}
+
+	function getZoomScale() {
+
+		return Math.pow( 0.95, scope.zoomSpeed );
+
+	}
+
+	function rotateLeft( angle ) {
+
+		sphericalDelta.theta -= angle;
+
+	}
+
+	function rotateUp( angle ) {
+
+		sphericalDelta.phi -= angle;
+
+	}
+
+	var panLeft = function() {
+
+		var v = new THREE.Vector3();
+
+		return function panLeft( distance, objectMatrix ) {
+
+			v.setFromMatrixColumn( objectMatrix, 0 ); // get X column of objectMatrix
+			v.multiplyScalar( - distance );
+
+			panOffset.add( v );
+
+		};
+
+	}();
+
+	var panUp = function() {
+
+		var v = new THREE.Vector3();
+
+		return function panUp( distance, objectMatrix ) {
+
+			v.setFromMatrixColumn( objectMatrix, 1 ); // get Y column of objectMatrix
+			v.multiplyScalar( distance );
+
+			panOffset.add( v );
+
+		};
+
+	}();
+
+	// deltaX and deltaY are in pixels; right and down are positive
+	var pan = function() {
+
+		var offset = new THREE.Vector3();
+
+		return function( deltaX, deltaY ) {
+
+			var element = scope.domElement === document ? scope.domElement.body : scope.domElement;
+
+			if ( scope.object instanceof THREE.PerspectiveCamera ) {
+
+				// perspective
+				var position = scope.object.position;
+				offset.copy( position ).sub( scope.target );
+				var targetDistance = offset.length();
+
+				// half of the fov is center to top of screen
+				targetDistance *= Math.tan( ( scope.object.fov / 2 ) * Math.PI / 180.0 );
+
+				// we actually don't use screenWidth, since perspective camera is fixed to screen height
+				panLeft( 2 * deltaX * targetDistance / element.clientHeight, scope.object.matrix );
+				panUp( 2 * deltaY * targetDistance / element.clientHeight, scope.object.matrix );
+
+			} else if ( scope.object instanceof THREE.OrthographicCamera ) {
+
+				// orthographic
+				panLeft( deltaX * ( scope.object.right - scope.object.left ) / scope.object.zoom / element.clientWidth, scope.object.matrix );
+				panUp( deltaY * ( scope.object.top - scope.object.bottom ) / scope.object.zoom / element.clientHeight, scope.object.matrix );
+
+			} else {
+
+				// camera neither orthographic nor perspective
+				console.warn( 'WARNING: OrbitControls.js encountered an unknown camera type - pan disabled.' );
+				scope.enablePan = false;
+
+			}
+
+		};
+
+	}();
+
+	function dollyIn( dollyScale ) {
+
+		if ( scope.object instanceof THREE.PerspectiveCamera ) {
+
+			scale /= dollyScale;
+
+		} else if ( scope.object instanceof THREE.OrthographicCamera ) {
+
+			scope.object.zoom = Math.max( scope.minZoom, Math.min( scope.maxZoom, scope.object.zoom * dollyScale ) );
+			scope.object.updateProjectionMatrix();
+			zoomChanged = true;
+
+		} else {
+
+			console.warn( 'WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.' );
+			scope.enableZoom = false;
+
+		}
+
+	}
+
+	function dollyOut( dollyScale ) {
+
+		if ( scope.object instanceof THREE.PerspectiveCamera ) {
+
+			scale *= dollyScale;
+
+		} else if ( scope.object instanceof THREE.OrthographicCamera ) {
+
+			scope.object.zoom = Math.max( scope.minZoom, Math.min( scope.maxZoom, scope.object.zoom / dollyScale ) );
+			scope.object.updateProjectionMatrix();
+			zoomChanged = true;
+
+		} else {
+
+			console.warn( 'WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.' );
+			scope.enableZoom = false;
+
+		}
+
+	}
+
+	//
+	// event callbacks - update the object state
+	//
+
+	function handleMouseDownRotate( event ) {
+
+		//console.log( 'handleMouseDownRotate' );
+
+		rotateStart.set( event.clientX, event.clientY );
+
+	}
+
+	function handleMouseDownDolly( event ) {
+
+		//console.log( 'handleMouseDownDolly' );
+
+		dollyStart.set( event.clientX, event.clientY );
+
+	}
+
+	function handleMouseDownPan( event ) {
+
+		//console.log( 'handleMouseDownPan' );
+
+		panStart.set( event.clientX, event.clientY );
+
+	}
+
+	function handleMouseMoveRotate( event ) {
+
+		//console.log( 'handleMouseMoveRotate' );
+
+		rotateEnd.set( event.clientX, event.clientY );
+		rotateDelta.subVectors( rotateEnd, rotateStart );
+
+		var element = scope.domElement === document ? scope.domElement.body : scope.domElement;
+
+		// rotating across whole screen goes 360 degrees around
+		rotateLeft( 2 * Math.PI * rotateDelta.x / element.clientWidth * scope.rotateSpeed );
+
+		// rotating up and down along whole screen attempts to go 360, but limited to 180
+		rotateUp( 2 * Math.PI * rotateDelta.y / element.clientHeight * scope.rotateSpeed );
+
+		rotateStart.copy( rotateEnd );
+
+		scope.update();
+
+	}
+
+	function handleMouseMoveDolly( event ) {
+
+		//console.log( 'handleMouseMoveDolly' );
+
+		dollyEnd.set( event.clientX, event.clientY );
+
+		dollyDelta.subVectors( dollyEnd, dollyStart );
+
+		if ( dollyDelta.y > 0 ) {
+
+			dollyIn( getZoomScale() );
+
+		} else if ( dollyDelta.y < 0 ) {
+
+			dollyOut( getZoomScale() );
+
+		}
+
+		dollyStart.copy( dollyEnd );
+
+		scope.update();
+
+	}
+
+	function handleMouseMovePan( event ) {
+
+		//console.log( 'handleMouseMovePan' );
+
+		panEnd.set( event.clientX, event.clientY );
+
+		panDelta.subVectors( panEnd, panStart );
+
+		pan( panDelta.x, panDelta.y );
+
+		panStart.copy( panEnd );
+
+		scope.update();
+
+	}
+
+	function handleMouseUp( event ) {
+
+		//console.log( 'handleMouseUp' );
+
+	}
+
+	function handleMouseWheel( event ) {
+
+		//console.log( 'handleMouseWheel' );
+
+		var delta = 0;
+
+		if ( event.wheelDelta !== undefined ) {
+
+			// WebKit / Opera / Explorer 9
+
+			delta = event.wheelDelta;
+
+		} else if ( event.detail !== undefined ) {
+
+			// Firefox
+
+			delta = - event.detail;
+
+		}
+
+		if ( delta > 0 ) {
+
+			dollyOut( getZoomScale() );
+
+		} else if ( delta < 0 ) {
+
+			dollyIn( getZoomScale() );
+
+		}
+
+		scope.update();
+
+	}
+
+	function handleKeyDown( event ) {
+
+		//console.log( 'handleKeyDown' );
+
+		switch ( event.keyCode ) {
+
+			case scope.keys.UP:
+				pan( 0, scope.keyPanSpeed );
+				scope.update();
+				break;
+
+			case scope.keys.BOTTOM:
+				pan( 0, - scope.keyPanSpeed );
+				scope.update();
+				break;
+
+			case scope.keys.LEFT:
+				pan( scope.keyPanSpeed, 0 );
+				scope.update();
+				break;
+
+			case scope.keys.RIGHT:
+				pan( - scope.keyPanSpeed, 0 );
+				scope.update();
+				break;
+
+		}
+
+	}
+
+	function handleTouchStartRotate( event ) {
+
+		//console.log( 'handleTouchStartRotate' );
+
+		rotateStart.set( event.touches[ 0 ].pageX, event.touches[ 0 ].pageY );
+
+	}
+
+	function handleTouchStartDolly( event ) {
+
+		//console.log( 'handleTouchStartDolly' );
+
+		var dx = event.touches[ 0 ].pageX - event.touches[ 1 ].pageX;
+		var dy = event.touches[ 0 ].pageY - event.touches[ 1 ].pageY;
+
+		var distance = Math.sqrt( dx * dx + dy * dy );
+
+		dollyStart.set( 0, distance );
+
+	}
+
+	function handleTouchStartPan( event ) {
+
+		//console.log( 'handleTouchStartPan' );
+
+		panStart.set( event.touches[ 0 ].pageX, event.touches[ 0 ].pageY );
+
+	}
+
+	function handleTouchMoveRotate( event ) {
+
+		//console.log( 'handleTouchMoveRotate' );
+
+		rotateEnd.set( event.touches[ 0 ].pageX, event.touches[ 0 ].pageY );
+		rotateDelta.subVectors( rotateEnd, rotateStart );
+
+		var element = scope.domElement === document ? scope.domElement.body : scope.domElement;
+
+		// rotating across whole screen goes 360 degrees around
+		rotateLeft( 2 * Math.PI * rotateDelta.x / element.clientWidth * scope.rotateSpeed );
+
+		// rotating up and down along whole screen attempts to go 360, but limited to 180
+		rotateUp( 2 * Math.PI * rotateDelta.y / element.clientHeight * scope.rotateSpeed );
+
+		rotateStart.copy( rotateEnd );
+
+		scope.update();
+
+	}
+
+	function handleTouchMoveDolly( event ) {
+
+		//console.log( 'handleTouchMoveDolly' );
+
+		var dx = event.touches[ 0 ].pageX - event.touches[ 1 ].pageX;
+		var dy = event.touches[ 0 ].pageY - event.touches[ 1 ].pageY;
+
+		var distance = Math.sqrt( dx * dx + dy * dy );
+
+		dollyEnd.set( 0, distance );
+
+		dollyDelta.subVectors( dollyEnd, dollyStart );
+
+		if ( dollyDelta.y > 0 ) {
+
+			dollyOut( getZoomScale() );
+
+		} else if ( dollyDelta.y < 0 ) {
+
+			dollyIn( getZoomScale() );
+
+		}
+
+		dollyStart.copy( dollyEnd );
+
+		scope.update();
+
+	}
+
+	function handleTouchMovePan( event ) {
+
+		//console.log( 'handleTouchMovePan' );
+
+		panEnd.set( event.touches[ 0 ].pageX, event.touches[ 0 ].pageY );
+
+		panDelta.subVectors( panEnd, panStart );
+
+		pan( panDelta.x, panDelta.y );
+
+		panStart.copy( panEnd );
+
+		scope.update();
+
+	}
+
+	function handleTouchEnd( event ) {
+
+		//console.log( 'handleTouchEnd' );
+
+	}
+
+	//
+	// event handlers - FSM: listen for events and reset state
+	//
+
+	function onMouseDown( event ) {
+
+		if ( scope.enabled === false ) return;
+
+		event.preventDefault();
+
+		if ( event.button === scope.mouseButtons.ORBIT ) {
+
+			if ( scope.enableRotate === false ) return;
+
+			handleMouseDownRotate( event );
+
+			state = STATE.ROTATE;
+
+		} else if ( event.button === scope.mouseButtons.ZOOM ) {
+
+			if ( scope.enableZoom === false ) return;
+
+			handleMouseDownDolly( event );
+
+			state = STATE.DOLLY;
+
+		} else if ( event.button === scope.mouseButtons.PAN ) {
+
+			if ( scope.enablePan === false ) return;
+
+			handleMouseDownPan( event );
+
+			state = STATE.PAN;
+
+		}
+
+		if ( state !== STATE.NONE ) {
+
+			document.addEventListener( 'mousemove', onMouseMove, false );
+			document.addEventListener( 'mouseup', onMouseUp, false );
+			document.addEventListener( 'mouseout', onMouseUp, false );
+
+			scope.dispatchEvent( startEvent );
+
+		}
+
+	}
+
+	function onMouseMove( event ) {
+
+		if ( scope.enabled === false ) return;
+
+		event.preventDefault();
+
+		if ( state === STATE.ROTATE ) {
+
+			if ( scope.enableRotate === false ) return;
+
+			handleMouseMoveRotate( event );
+
+		} else if ( state === STATE.DOLLY ) {
+
+			if ( scope.enableZoom === false ) return;
+
+			handleMouseMoveDolly( event );
+
+		} else if ( state === STATE.PAN ) {
+
+			if ( scope.enablePan === false ) return;
+
+			handleMouseMovePan( event );
+
+		}
+
+	}
+
+	function onMouseUp( event ) {
+
+		if ( scope.enabled === false ) return;
+
+		handleMouseUp( event );
+
+		document.removeEventListener( 'mousemove', onMouseMove, false );
+		document.removeEventListener( 'mouseup', onMouseUp, false );
+		document.removeEventListener( 'mouseout', onMouseUp, false );
+
+		scope.dispatchEvent( endEvent );
+
+		state = STATE.NONE;
+
+	}
+
+	function onMouseWheel( event ) {
+
+		if ( scope.enabled === false || scope.enableZoom === false || ( state !== STATE.NONE && state !== STATE.ROTATE ) ) return;
+
+		event.preventDefault();
+		event.stopPropagation();
+
+		handleMouseWheel( event );
+
+		scope.dispatchEvent( startEvent ); // not sure why these are here...
+		scope.dispatchEvent( endEvent );
+
+	}
+
+	function onKeyDown( event ) {
+
+		if ( scope.enabled === false || scope.enableKeys === false || scope.enablePan === false ) return;
+
+		handleKeyDown( event );
+
+	}
+
+	function onTouchStart( event ) {
+
+		if ( scope.enabled === false ) return;
+
+		switch ( event.touches.length ) {
+
+			case 1:	// one-fingered touch: rotate
+
+				if ( scope.enableRotate === false ) return;
+
+				handleTouchStartRotate( event );
+
+				state = STATE.TOUCH_ROTATE;
+
+				break;
+
+			case 2:	// two-fingered touch: dolly
+
+				if ( scope.enableZoom === false ) return;
+
+				handleTouchStartDolly( event );
+
+				state = STATE.TOUCH_DOLLY;
+
+				break;
+
+			case 3: // three-fingered touch: pan
+
+				if ( scope.enablePan === false ) return;
+
+				handleTouchStartPan( event );
+
+				state = STATE.TOUCH_PAN;
+
+				break;
+
+			default:
+
+				state = STATE.NONE;
+
+		}
+
+		if ( state !== STATE.NONE ) {
+
+			scope.dispatchEvent( startEvent );
+
+		}
+
+	}
+
+	function onTouchMove( event ) {
+
+		if ( scope.enabled === false ) return;
+
+		event.preventDefault();
+		event.stopPropagation();
+
+		switch ( event.touches.length ) {
+
+			case 1: // one-fingered touch: rotate
+
+				if ( scope.enableRotate === false ) return;
+				if ( state !== STATE.TOUCH_ROTATE ) return; // is this needed?...
+
+				handleTouchMoveRotate( event );
+
+				break;
+
+			case 2: // two-fingered touch: dolly
+
+				if ( scope.enableZoom === false ) return;
+				if ( state !== STATE.TOUCH_DOLLY ) return; // is this needed?...
+
+				handleTouchMoveDolly( event );
+
+				break;
+
+			case 3: // three-fingered touch: pan
+
+				if ( scope.enablePan === false ) return;
+				if ( state !== STATE.TOUCH_PAN ) return; // is this needed?...
+
+				handleTouchMovePan( event );
+
+				break;
+
+			default:
+
+				state = STATE.NONE;
+
+		}
+
+	}
+
+	function onTouchEnd( event ) {
+
+		if ( scope.enabled === false ) return;
+
+		handleTouchEnd( event );
+
+		scope.dispatchEvent( endEvent );
+
+		state = STATE.NONE;
+
+	}
+
+	function onContextMenu( event ) {
+
+		event.preventDefault();
+
+	}
+
+	//
+
+	scope.domElement.addEventListener( 'contextmenu', onContextMenu, false );
+
+	scope.domElement.addEventListener( 'mousedown', onMouseDown, false );
+	scope.domElement.addEventListener( 'mousewheel', onMouseWheel, false );
+	scope.domElement.addEventListener( 'MozMousePixelScroll', onMouseWheel, false ); // firefox
+
+	scope.domElement.addEventListener( 'touchstart', onTouchStart, false );
+	scope.domElement.addEventListener( 'touchend', onTouchEnd, false );
+	scope.domElement.addEventListener( 'touchmove', onTouchMove, false );
+
+	window.addEventListener( 'keydown', onKeyDown, false );
+
+	// force an update at start
+
+	this.update();
+
+};
+
+THREE.OrbitControls.prototype = Object.create( THREE.EventDispatcher.prototype );
+THREE.OrbitControls.prototype.constructor = THREE.OrbitControls;
+
+Object.defineProperties( THREE.OrbitControls.prototype, {
+
+	center: {
+
+		get: function () {
+
+			console.warn( 'THREE.OrbitControls: .center has been renamed to .target' );
+			return this.target;
+
+		}
+
+	},
+
+	// backward compatibility
+
+	noZoom: {
+
+		get: function () {
+
+			console.warn( 'THREE.OrbitControls: .noZoom has been deprecated. Use .enableZoom instead.' );
+			return ! this.enableZoom;
+
+		},
+
+		set: function ( value ) {
+
+			console.warn( 'THREE.OrbitControls: .noZoom has been deprecated. Use .enableZoom instead.' );
+			this.enableZoom = ! value;
+
+		}
+
+	},
+
+	noRotate: {
+
+		get: function () {
+
+			console.warn( 'THREE.OrbitControls: .noRotate has been deprecated. Use .enableRotate instead.' );
+			return ! this.enableRotate;
+
+		},
+
+		set: function ( value ) {
+
+			console.warn( 'THREE.OrbitControls: .noRotate has been deprecated. Use .enableRotate instead.' );
+			this.enableRotate = ! value;
+
+		}
+
+	},
+
+	noPan: {
+
+		get: function () {
+
+			console.warn( 'THREE.OrbitControls: .noPan has been deprecated. Use .enablePan instead.' );
+			return ! this.enablePan;
+
+		},
+
+		set: function ( value ) {
+
+			console.warn( 'THREE.OrbitControls: .noPan has been deprecated. Use .enablePan instead.' );
+			this.enablePan = ! value;
+
+		}
+
+	},
+
+	noKeys: {
+
+		get: function () {
+
+			console.warn( 'THREE.OrbitControls: .noKeys has been deprecated. Use .enableKeys instead.' );
+			return ! this.enableKeys;
+
+		},
+
+		set: function ( value ) {
+
+			console.warn( 'THREE.OrbitControls: .noKeys has been deprecated. Use .enableKeys instead.' );
+			this.enableKeys = ! value;
+
+		}
+
+	},
+
+	staticMoving : {
+
+		get: function () {
+
+			console.warn( 'THREE.OrbitControls: .staticMoving has been deprecated. Use .enableDamping instead.' );
+			return ! this.enableDamping;
+
+		},
+
+		set: function ( value ) {
+
+			console.warn( 'THREE.OrbitControls: .staticMoving has been deprecated. Use .enableDamping instead.' );
+			this.enableDamping = ! value;
+
+		}
+
+	},
+
+	dynamicDampingFactor : {
+
+		get: function () {
+
+			console.warn( 'THREE.OrbitControls: .dynamicDampingFactor has been renamed. Use .dampingFactor instead.' );
+			return this.dampingFactor;
+
+		},
+
+		set: function ( value ) {
+
+			console.warn( 'THREE.OrbitControls: .dynamicDampingFactor has been renamed. Use .dampingFactor instead.' );
+			this.dampingFactor = value;
+
+		}
+
+	}
+
+} );

dataloaders/pymo/mocapplayer/libs/threejs/dat.gui.min.js

@@ -0,0 +1,14 @@
+/**
+ * dat-gui JavaScript Controller Library
+ * http://code.google.com/p/dat-gui
+ *
+ * Copyright 2011 Data Arts Team, Google Creative Lab
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ */
+!function(e,t){"object"==typeof exports&&"object"==typeof module?module.exports=t():"function"==typeof define&&define.amd?define([],t):"object"==typeof exports?exports.dat=t():e.dat=t()}(this,function(){return function(e){function t(o){if(n[o])return n[o].exports;var i=n[o]={exports:{},id:o,loaded:!1};return e[o].call(i.exports,i,i.exports,t),i.loaded=!0,i.exports}var n={};return t.m=e,t.c=n,t.p="",t(0)}([function(e,t,n){"use strict";t.__esModule=!0,t["default"]=n(1),e.exports=t["default"]},function(e,t,n){"use strict";t.__esModule=!0,t["default"]={color:{Color:n(2),math:n(6),interpret:n(3)},controllers:{Controller:n(7),BooleanController:n(8),OptionController:n(10),StringController:n(11),NumberController:n(12),NumberControllerBox:n(13),NumberControllerSlider:n(14),FunctionController:n(15),ColorController:n(16)},dom:{dom:n(9)},gui:{GUI:n(17)},GUI:n(17)},e.exports=t["default"]},function(e,t,n){"use strict";function o(e){return e&&e.__esModule?e:{"default":e}}function i(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function a(e,t,n){Object.defineProperty(e,t,{get:function(){return"RGB"===this.__state.space?this.__state[t]:(p.recalculateRGB(this,t,n),this.__state[t])},set:function(e){"RGB"!==this.__state.space&&(p.recalculateRGB(this,t,n),this.__state.space="RGB"),this.__state[t]=e}})}function r(e,t){Object.defineProperty(e,t,{get:function(){return"HSV"===this.__state.space?this.__state[t]:(p.recalculateHSV(this),this.__state[t])},set:function(e){"HSV"!==this.__state.space&&(p.recalculateHSV(this),this.__state.space="HSV"),this.__state[t]=e}})}t.__esModule=!0;var s=n(3),l=o(s),d=n(6),u=o(d),c=n(4),f=o(c),h=n(5),_=o(h),p=function(){function e(){if(i(this,e),this.__state=l["default"].apply(this,arguments),this.__state===!1)throw new Error("Failed to interpret color arguments");this.__state.a=this.__state.a||1}return e.prototype.toString=function(){return f["default"](this)},e.prototype.toOriginal=function(){return this.__state.conversion.write(this)},e}();p.recalculateRGB=function(e,t,n){if("HEX"===e.__state.space)e.__state[t]=u["default"].component_from_hex(e.__state.hex,n);else{if("HSV"!==e.__state.space)throw new Error("Corrupted color state");_["default"].extend(e.__state,u["default"].hsv_to_rgb(e.__state.h,e.__state.s,e.__state.v))}},p.recalculateHSV=function(e){var t=u["default"].rgb_to_hsv(e.r,e.g,e.b);_["default"].extend(e.__state,{s:t.s,v:t.v}),_["default"].isNaN(t.h)?_["default"].isUndefined(e.__state.h)&&(e.__state.h=0):e.__state.h=t.h},p.COMPONENTS=["r","g","b","h","s","v","hex","a"],a(p.prototype,"r",2),a(p.prototype,"g",1),a(p.prototype,"b",0),r(p.prototype,"h"),r(p.prototype,"s"),r(p.prototype,"v"),Object.defineProperty(p.prototype,"a",{get:function(){return this.__state.a},set:function(e){this.__state.a=e}}),Object.defineProperty(p.prototype,"hex",{get:function(){return"HEX"!==!this.__state.space&&(this.__state.hex=u["default"].rgb_to_hex(this.r,this.g,this.b)),this.__state.hex},set:function(e){this.__state.space="HEX",this.__state.hex=e}}),t["default"]=p,e.exports=t["default"]},function(e,t,n){"use strict";function o(e){return e&&e.__esModule?e:{"default":e}}t.__esModule=!0;var i=n(4),a=o(i),r=n(5),s=o(r),l=[{litmus:s["default"].isString,conversions:{THREE_CHAR_HEX:{read:function(e){var t=e.match(/^#([A-F0-9])([A-F0-9])([A-F0-9])$/i);return null!==t&&{space:"HEX",hex:parseInt("0x"+t[1].toString()+t[1].toString()+t[2].toString()+t[2].toString()+t[3].toString()+t[3].toString(),0)}},write:a["default"]},SIX_CHAR_HEX:{read:function(e){var t=e.match(/^#([A-F0-9]{6})$/i);return null!==t&&{space:"HEX",hex:parseInt("0x"+t[1].toString(),0)}},write:a["default"]},CSS_RGB:{read:function(e){var 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t=I["default"].getOffset(e.__ul).top,n=0;U["default"].each(e.__ul.childNodes,function(t){e.autoPlace&&t===e.__save_row||(n+=I["default"].getHeight(t))}),window.innerHeight-t-Y<n?(I["default"].addClass(e.domElement,ne.CLASS_TOO_TALL),e.__ul.style.height=window.innerHeight-t-Y+"px"):(I["default"].removeClass(e.domElement,ne.CLASS_TOO_TALL),e.__ul.style.height="auto")}e.__resize_handle&&U["default"].defer(function(){e.__resize_handle.style.height=e.__ul.offsetHeight+"px"}),e.__closeButton&&(e.__closeButton.style.width=e.width+"px")},200),remember:function(){if(U["default"].isUndefined(q)&&(q=new V["default"],q.domElement.innerHTML=w["default"]),this.parent)throw new Error("You can only call remember on a top level GUI.");var e=this;U["default"].each(Array.prototype.slice.call(arguments),function(t){0===e.__rememberedObjects.length&&f(e),e.__rememberedObjects.indexOf(t)===-1&&e.__rememberedObjects.push(t)}),this.autoPlace&&_(this,this.width)},getRoot:function(){for(var e=this;e.parent;)e=e.parent;return e},getSaveObject:function(){var e=this.load;return e.closed=this.closed,this.__rememberedObjects.length>0&&(e.preset=this.preset,e.remembered||(e.remembered={}),e.remembered[this.preset]=p(this)),e.folders={},U["default"].each(this.__folders,function(t,n){e.folders[n]=t.getSaveObject()}),e},save:function(){this.load.remembered||(this.load.remembered={}),this.load.remembered[this.preset]=p(this),a(this,!1),this.saveToLocalStorageIfPossible()},saveAs:function(e){this.load.remembered||(this.load.remembered={},this.load.remembered[Q]=p(this,!0)),this.load.remembered[e]=p(this),this.preset=e,u(this,e,!0),this.saveToLocalStorageIfPossible()},revert:function(e){U["default"].each(this.__controllers,function(t){this.getRoot().load.remembered?s(e||this.getRoot(),t):t.setValue(t.initialValue),t.__onFinishChange&&t.__onFinishChange.call(t,t.getValue())},this),U["default"].each(this.__folders,function(e){e.revert(e)}),e||a(this.getRoot(),!1)},listen:function(e){var t=0===this.__listening.length;this.__listening.push(e),t&&b(this.__listening)},updateDisplay:function(){U["default"].each(this.__controllers,function(e){e.updateDisplay()}),U["default"].each(this.__folders,function(e){e.updateDisplay()})}}),e.exports=ne},function(e,t){"use strict";e.exports={load:function(e,t){var n=t||document,o=n.createElement("link");o.type="text/css",o.rel="stylesheet",o.href=e,n.getElementsByTagName("head")[0].appendChild(o)},inject:function(e,t){var n=t||document,o=document.createElement("style");o.type="text/css",o.innerHTML=e;var i=n.getElementsByTagName("head")[0];try{i.appendChild(o)}catch(a){}}}},function(e,t){e.exports='<div id=dg-save class="dg dialogue">Here\'s the new load parameter for your <code>GUI</code>\'s constructor:<textarea id=dg-new-constructor></textarea><div id=dg-save-locally><input id=dg-local-storage type="checkbox"> Automatically save values to <code>localStorage</code> on exit.<div id=dg-local-explain>The values saved to <code>localStorage</code> will override those passed to <code>dat.GUI</code>\'s constructor. This makes it easier to work incrementally, but <code>localStorage</code> is fragile, and your friends may not see the same values you do.</div></div></div>'},function(e,t,n){"use strict";function o(e){return e&&e.__esModule?e:{"default":e}}t.__esModule=!0;var i=n(10),a=o(i),r=n(13),s=o(r),l=n(14),d=o(l),u=n(11),c=o(u),f=n(15),h=o(f),_=n(8),p=o(_),m=n(5),b=o(m),g=function(e,t){var n=e[t];return b["default"].isArray(arguments[2])||b["default"].isObject(arguments[2])?new a["default"](e,t,arguments[2]):b["default"].isNumber(n)?b["default"].isNumber(arguments[2])&&b["default"].isNumber(arguments[3])?b["default"].isNumber(arguments[4])?new d["default"](e,t,arguments[2],arguments[3],arguments[4]):new d["default"](e,t,arguments[2],arguments[3]):b["default"].isNumber(arguments[4])?new s["default"](e,t,{min:arguments[2],max:arguments[3],step:arguments[4]}):new s["default"](e,t,{min:arguments[2],max:arguments[3]}):b["default"].isString(n)?new c["default"](e,t):b["default"].isFunction(n)?new h["default"](e,t,""):b["default"].isBoolean(n)?new p["default"](e,t):null};t["default"]=g,e.exports=t["default"]},function(e,t){"use strict";function n(e){setTimeout(e,1e3/60)}t.__esModule=!0,t["default"]=window.requestAnimationFrame||window.webkitRequestAnimationFrame||window.mozRequestAnimationFrame||window.oRequestAnimationFrame||window.msRequestAnimationFrame||n,e.exports=t["default"]},function(e,t,n){"use strict";function o(e){return e&&e.__esModule?e:{"default":e}}function i(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}t.__esModule=!0;var a=n(9),r=o(a),s=n(5),l=o(s),d=function(){function e(){i(this,e),this.backgroundElement=document.createElement("div"),l["default"].extend(this.backgroundElement.style,{backgroundColor:"rgba(0,0,0,0.8)",top:0,left:0,display:"none",zIndex:"1000",opacity:0,WebkitTransition:"opacity 0.2s linear",transition:"opacity 0.2s linear"}),r["default"].makeFullscreen(this.backgroundElement),this.backgroundElement.style.position="fixed",this.domElement=document.createElement("div"),l["default"].extend(this.domElement.style,{position:"fixed",display:"none",zIndex:"1001",opacity:0,WebkitTransition:"-webkit-transform 0.2s ease-out, opacity 0.2s linear",transition:"transform 0.2s ease-out, opacity 0.2s linear"}),document.body.appendChild(this.backgroundElement),document.body.appendChild(this.domElement);var t=this;r["default"].bind(this.backgroundElement,"click",function(){t.hide()})}return e.prototype.show=function(){var e=this;this.backgroundElement.style.display="block",this.domElement.style.display="block",this.domElement.style.opacity=0,this.domElement.style.webkitTransform="scale(1.1)",this.layout(),l["default"].defer(function(){e.backgroundElement.style.opacity=1,e.domElement.style.opacity=1,e.domElement.style.webkitTransform="scale(1)"})},e.prototype.hide=function t(){var e=this,t=function 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.main{overflow:hidden}.dg.main{-webkit-transition:opacity .1s linear;transition:opacity .1s linear}.dg.main.taller-than-window{overflow-y:auto}.dg.main.taller-than-window .close-button{opacity:1;margin-top:-1px;border-top:1px solid #2c2c2c}.dg.main ul.closed .close-button{opacity:1!important}.dg.main .close-button.drag,.dg.main:hover .close-button{opacity:1}.dg.main .close-button{-webkit-transition:opacity .1s linear;transition:opacity .1s linear;border:0;position:absolute;line-height:19px;height:20px;cursor:pointer;text-align:center;background-color:#000}.dg.main .close-button:hover{background-color:#111}.dg.a{float:right;margin-right:15px;overflow-x:hidden}.dg.a.has-save>ul{margin-top:27px}.dg.a.has-save>ul.closed{margin-top:0}.dg.a .save-row{position:fixed;top:0;z-index:1002}.dg li{-webkit-transition:height .1s ease-out;transition:height .1s ease-out}.dg li:not(.folder){cursor:auto;height:27px;line-height:27px;overflow:hidden;padding:0 4px 0 5px}.dg li.folder{padding:0;border-left:4px solid transparent}.dg li.title{cursor:pointer;margin-left:-4px}.dg .closed li:not(.title),.dg .closed ul li,.dg .closed ul li>*{height:0;overflow:hidden;border:0}.dg .cr{clear:both;padding-left:3px;height:27px}.dg .property-name{cursor:default;float:left;clear:left;width:40%;overflow:hidden;text-overflow:ellipsis}.dg .c{float:left;width:60%}.dg .c input[type=text]{border:0;margin-top:4px;padding:3px;width:100%;float:right}.dg .has-slider input[type=text]{width:30%;margin-left:0}.dg .slider{float:left;width:66%;margin-left:-5px;margin-right:0;height:19px;margin-top:4px}.dg .slider-fg{height:100%}.dg .c input[type=checkbox]{margin-top:9px}.dg .c select{margin-top:5px}.dg .cr.boolean,.dg .cr.boolean *,.dg .cr.function,.dg .cr.function *,.dg .cr.function .property-name{cursor:pointer}.dg .selector{display:none;position:absolute;margin-left:-9px;margin-top:23px;z-index:10}.dg .c:hover .selector,.dg .selector.drag{display:block}.dg 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dataloaders/pymo/mocapplayer/playBuffer.html

@@ -0,0 +1,418 @@
+<!DOCTYPE html>
+<!-- GrooveNet Player - V 0.3
+By Omid Alemi
+ -->
+<html lang="en">
+
+<head>
+    <title>BVH Player</title>
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, user-scalable=no, minimum-scale=1.0, maximum-scale=1.0">
+    <style>
+        body {
+            margin:0;
+            overflow: hidden;
+        }
+
+        #metaoverlay {
+            width: 600px;
+            height: 150px;
+            background-color: rgba(10,10,10,0.5);
+            position: fixed;
+            bottom: 30px;
+            margin: 0 auto;
+            left: 20px;
+            display: none;
+            border-radius: 10px;
+        }
+
+        #labels {
+            font-family: sans-serif;
+            width: 300px;
+            height: 50%;
+            color: #e1e1e1;
+            line-height: 1.5;
+            /* border: 1px solid #f1f1f1; */
+            display: flex;
+            margin: 20px;
+            padding: 10px;
+        }
+        .bar {
+            fill: rgba(80, 180, 240, 0.99);
+        }
+
+        svg {
+            /* border: 1px solid red; */
+        }
+    </style>
+    <link rel="stylesheet" href="styles/pace.css"></link>
+
+    <script src="https://d3js.org/d3.v4.min.js"></script>
+
+    <script src="libs/pace.min.js"></script>
+    <script src="libs/math.min.js"></script>
+    <script src="libs/jquery.min.js"></script>
+    <script src="libs/threejs/three.min.js"></script>
+    <script src="libs/threejs/OrbitControls.js"></script>
+    <script src="libs/papaparse.min.js"></script>
+
+    <script src="libs/mocapjs.js"></script>
+    <script src="js/skeletonFactory.js"></script>
+</head>
+
+<body>
+    <div id="metaoverlay">
+        <div id="labels"></div>
+        <div id="labelsChart"></div>
+    </div>
+    
+    <script type="text/javascript">
+        var characters = [];
+        var playing = false;
+        var showMeta = false;
+        var metadata = [];
+        var chart = {};
+
+
+        $(document).on("keypress", function(e) {
+            if (e.charCode == 32)
+                playing = !playing;
+
+            characters.forEach(function(c) {
+                c.playing = playing;
+
+                if (playing)
+                    c.animStartTimeRef = Date.now();
+                else
+                    c.animOffset = c.animIndex;
+            });
+
+        });
+
+        $(window).on("resize", function(e) {
+            camera.aspect = window.innerWidth / window.innerHeight;
+            camera.updateProjectionMatrix();
+
+           renderer.setSize(window.innerWidth, window.innerHeight);
+        });
+
+        var getUrlParameter = function getUrlParameter(sParam) {
+        var sPageURL = decodeURIComponent(window.location.search.substring(1)),
+            sURLVariables = sPageURL.split('&'),
+            sParameterName,
+            i;
+
+        for (i = 0; i < sURLVariables.length; i++) {
+            sParameterName = sURLVariables[i].split('=');            
+            if (sParameterName[0] === sParam) {
+                return sParameterName[1] === undefined ? true : sParameterName[1];
+            }
+        }
+    };
+    </script>
+
+    <script>
+        var scene, camera, renderer;
+        var geometry, material, mesh;
+        var played_count = 0;
+
+        function set_the_scene3() {
+			
+            // Add the light            
+			light = new THREE.PointLight(0xffffff, 0.9, 0);
+			light.position.set(0, 160, 40);
+			scene.add(light);
+
+			// Create a grid for the floor
+			var size = 200,
+				step = 20;
+
+            // Draw the plane
+			var planeGeometry = new THREE.PlaneGeometry(size * 2, size * 2);
+			var planeMaterial = new THREE.MeshPhongMaterial({
+				color: 0x444444,
+				emissive: 0x000000,
+				specular: 0x111111,
+				side: THREE.DoubleSide,
+				transparent: true,
+				opacity: 0.6
+			});
+			var plane = new THREE.Mesh(planeGeometry, planeMaterial);
+			plane.position.set(0, 0, 0);
+			plane.rotation.set(math.pi / 2, 0, 0);
+			scene.add(plane);
+
+            //Draw the lines
+			var lineGeometry = new THREE.Geometry();
+			var lineMaterial = new THREE.LineBasicMaterial({
+				color: 0x555555,
+				linewidth: 1.2
+			});
+			for (var i = -size; i <= size; i += step) {
+				lineGeometry.vertices.push(new THREE.Vector3(-size, -0.04, i));
+				lineGeometry.vertices.push(new THREE.Vector3(size, -0.04, i));
+				lineGeometry.vertices.push(new THREE.Vector3(i, -0.04, -size));
+				lineGeometry.vertices.push(new THREE.Vector3(i, -0.04, size));
+			}
+
+			var line = new THREE.LineSegments(lineGeometry, lineMaterial);
+			scene.add(line);
+		}
+
+        function add_basic_lights() {
+            // Add the light            
+			var light = new THREE.HemisphereLight(0xffffff, 0x000000, 0.9);            
+            scene.add(light);
+        }
+
+        function add_floor_theo() {
+            var textureLoader = new THREE.TextureLoader();
+
+            var texture = textureLoader.load('theo_floor1.jpg', function(tt) {    
+            });
+
+            texture.wrapS = THREE.RepeatWrapping;
+            texture.wrapT = THREE.RepeatWrapping;
+            texture.repeat = new THREE.Vector2(1,1.66);
+
+
+            var material = new THREE.MeshPhongMaterial({
+                color: 0xffffff,
+                specular: 0xffffff,
+                shininess: 100,
+                map: texture,
+
+            });
+            var geometry = new THREE.CubeGeometry(300, 300, 5);
+
+            var mesh = new THREE.Mesh(geometry, material);
+            mesh.position.y = -2;
+            mesh.rotation.x = -Math.PI / 2;
+            scene.add(mesh);
+        }
+
+        function add_floor_grid(){
+            // Create a grid for the floor
+			var size = 400,
+				step = 40;
+
+            // Draw the plane
+			var planeGeometry = new THREE.PlaneGeometry(size * 2, size * 2);
+			var planeMaterial = new THREE.MeshPhongMaterial({
+				color: 0x212121,
+				emissive: 0x333333,
+				specular: 0x222222,
+				side: THREE.DoubleSide,
+				transparent: true,
+				opacity: 0.3
+			});
+			var plane = new THREE.Mesh(planeGeometry, planeMaterial);
+			plane.position.set(0, 0, 0);
+			plane.rotation.set(math.pi / 2, 0, 0);
+			scene.add(plane);
+
+            //Draw the lines
+			var lineGeometry = new THREE.Geometry();
+			var lineMaterial = new THREE.LineBasicMaterial({
+				color: 0x333333,
+				linewidth: 1.2
+			});
+			for (var i = -size; i <= size; i += step) {
+				lineGeometry.vertices.push(new THREE.Vector3(-size, -0.04, i));
+				lineGeometry.vertices.push(new THREE.Vector3(size, -0.04, i));
+				lineGeometry.vertices.push(new THREE.Vector3(i, -0.04, -size));
+				lineGeometry.vertices.push(new THREE.Vector3(i, -0.04, size));
+			}
+
+			var line = new THREE.LineSegments(lineGeometry, lineMaterial);
+			scene.add(line);
+        }
+
+        function init(cz) {
+            scene = new THREE.Scene();
+            camera = new THREE.PerspectiveCamera(30, window.innerWidth / window.innerHeight, 1, 4000);
+            camera.position.set(0, 250, 0);
+            camera.position.z = cz;
+            scene.add(camera);
+
+            // set_the_scene3();
+        }
+
+        function loadBVHFile(bvhURL, cname, jm, bm, scale) {            
+            var c2 = new BVHCharacter(cname, jm, bm, makeJointGeometry_Dode, makeBoneGeometry_Cylinder2);
+            // c2.setOriginPosition(0, 0, 0);
+            c2.skelScale = scale;
+            c2.loadFromURL(bvhURL, function() {
+                scene.add(c2.skeleton);
+            });
+
+            characters.push(c2);
+        }
+
+        function loadPosFile(csvURL, cname, jm, bm, scale, fr) {            
+            var c2 = new C3DCharacter(cname, markermaterial, makeJointGeometry_Sphere1);
+            // c2.setOriginPosition(0, 0, 0);
+            c2.scale = scale;
+            c2.frameTime = fr;
+            c2.loadFromURL(csvURL, function() {});
+
+            characters.push(c2);
+        }
+
+        function loadPosBuffer(data, cname, jm, bm, scale, fr) {            
+            var c2 = new C3DCharacter(cname, bonematerial5, makeJointGeometry_SphereX(2.5));
+            // c2.setOriginPosition(0, 0, 0);
+            c2.scale = scale;
+            c2.frameTime = fr;
+            c2.loadFromBuffer(data, function() {});
+
+            characters.push(c2);
+        }
+
+        function initRenderer() {
+            renderer = new THREE.WebGLRenderer({
+                antialias: true
+            });
+            renderer.setSize(window.innerWidth, window.innerHeight);
+            //renderer.setSize(600, 500);
+            renderer.gammaInput = true;
+            renderer.gammaOutput = true;
+            renderer.setPixelRatio(window.devicePixelRatio);
+            renderer.setClearColor(0x222222, 1);
+            // renderer.setClearColor(0xffffff, 1);
+
+            
+            document.body.appendChild(renderer.domElement);
+
+           controls = new THREE.OrbitControls(camera, renderer.domElement);
+        }
+
+        function animate() {
+            // if (!playing)
+            //     return;
+
+            requestAnimationFrame(animate);
+
+            characters.forEach(function(c) {
+                // console.log(c.playing);
+                if (1 && c.ready) {
+                    if (c.playing) {
+                        c.animIndex = c.animOffset + Math.floor((Date.now() - c.animStartTimeRef) / c.frameTime / 1000);
+
+                        if (c.animIndex >= c.frameCount-1) {
+                            c.animOffset = 0;
+                            c.animStartTimeRef = Date.now();
+                            c.animIndex = 0;
+                            played_count++;
+                            // if (played_count> 1)
+                            //     c.playing = false;
+
+                        }
+                        c.animFrame(c.animIndex);
+                        
+                        if (showMeta) {
+                            var labeldata = '';
+                            for (var i=0; i<metadata[c.animIndex].length;i++) {
+                                var v = Math.round(metadata[c.animIndex][i]*100)/100;
+                                labeldata+= 'Label '+i+ ':   '+ v+'<br/>';
+                            }                            
+                            document.getElementById('labels').innerHTML = labeldata;
+
+                            updateChart(metadata[c.animIndex]); 
+                        }                        
+                    }
+                }
+            });
+    
+            renderer.render(scene, camera);
+        }
+
+        function makeLabelsChart() {
+            var metadataFrame = metadata[0];
+
+            chart.width = 200;
+            chart.height = 150;
+            
+            chart.svg = d3.select("#labelsChart")
+                        .append("svg")
+                        .attr("width", 200)
+                        .attr("height", 150);            
+            
+            chart.x = d3.scaleBand().rangeRound([0, chart.width]).padding(0.1),
+            chart.y = d3.scaleLinear().rangeRound([chart.height, 0]),
+            chart.h = d3.scaleLinear().rangeRound([0, chart.height/2]);;      
+
+            chart.x.domain([0, 1, 2, 3]);
+            chart.y.domain([-2, 2]);
+            chart.h.domain([0, 2]);
+
+            var g = chart.svg.append("g");                        
+            
+            g.append("line")
+                .attr('x1', 0)
+                .attr('x2', chart.width)
+                .attr('y1', chart.height/2)
+                .attr('y2', chart.height/2)
+                .attr('stroke', '#f1f1f1');
+
+            g.selectAll(".bar")
+                .data(metadataFrame)
+                .enter().append("rect")
+                .attr("class", "bar")
+                .attr("x", function(d, i) { return chart.x(i); })
+                .attr("y", function(d, i) { return chart.y(Math.max(0, d)); })
+                .attr("width", 20)
+                .attr("height", function(d) { return  chart.h(Math.abs(d)); });
+        }
+
+        function updateChart(metadataFrame) {
+            var g = chart.svg.select("g");                
+
+            g.selectAll(".bar")
+                .data(metadataFrame)                
+                .attr("class", "bar")
+                .attr("x", function(d, i) { return chart.x(i); })
+                .attr("y", function(d, i) { return chart.y(Math.max(0, d)); })
+                .attr("width", 20)
+                .attr("height", function(d) { return  chart.h(Math.abs(d)); });
+        }
+
+        function start(dataBuffer, metadata, cz, scale, frameTime) {        
+            if (cz === undefined)
+                cz = 550;
+            
+            if (scale === undefined)
+                scale = 1.0;
+            
+            if (frameTime === undefined)
+                frameTime = 1/120;
+            
+            init(cz);
+            add_basic_lights();
+            add_floor_grid();
+            initRenderer();
+            
+            if (metadata.length > 1  ) {
+                showMeta = true;
+                document.getElementById("metaoverlay").style.display="flex";
+                // console.log(metadata.length);
+                makeLabelsChart();
+            }
+
+            if (dataBuffer !==  undefined) {
+                console.log("Loading from internal bufffer");
+                // console.log(dataBuffer.length);
+                loadPosBuffer(dataBuffer, 'Fig', jointmaterial4, bm_a, scale, frameTime); 
+
+            }
+            
+            
+
+            animate();
+        }
+    </script>
+
+    <script src="data.js"></script>
+</body>
+
+</html>

dataloaders/pymo/mocapplayer/playURL.html

@@ -0,0 +1,269 @@
+<!DOCTYPE html>
+<!-- GrooveNet Player - V 0.3
+By Omid Alemi
+ -->
+<html lang="en">
+
+<head>
+    <title>BVH Player</title>
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, user-scalable=no, minimum-scale=1.0, maximum-scale=1.0">
+    <style>
+
+    </style>
+    <link rel="stylesheet" href="styles/pace.css"></link>
+
+    <script src="libs/pace.min.js"></script>
+    <script src="libs/math.min.js"></script>
+    <script src="libs/jquery.min.js"></script>
+    <script src="libs/threejs/three.min.js"></script>
+    <script src="libs/threejs/OrbitControls.js"></script>
+    <script src="libs/papaparse.min.js"></script>
+
+    <script src="libs/mocapjs.js"></script>
+    <script src="js/skeletonFactory.js"></script>
+</head>
+
+<body>
+    <script type="text/javascript">
+        var characters = [];
+        var playing = false;
+
+
+        $(document).on("keypress", function(e) {
+            if (e.charCode == 32)
+                playing = !playing;
+
+            characters.forEach(function(c) {
+                c.playing = playing;
+
+                if (playing)
+                    c.animStartTimeRef = Date.now();
+                else
+                    c.animOffset = c.animIndex;
+            });
+
+        });
+
+        $(window).on("resize", function(e) {
+            camera.aspect = window.innerWidth / window.innerHeight;
+            camera.updateProjectionMatrix();
+
+           renderer.setSize(window.innerWidth, window.innerHeight);
+        });
+
+        var getUrlParameter = function getUrlParameter(sParam) {
+        var sPageURL = decodeURIComponent(window.location.search.substring(1)),
+            sURLVariables = sPageURL.split('&'),
+            sParameterName,
+            i;
+
+        for (i = 0; i < sURLVariables.length; i++) {
+            sParameterName = sURLVariables[i].split('=');            
+            if (sParameterName[0] === sParam) {
+                return sParameterName[1] === undefined ? true : sParameterName[1];
+            }
+        }
+    };
+    </script>
+
+    <script>
+        var scene, camera, renderer;
+        var geometry, material, mesh;
+        var played_count = 0;
+
+        function set_the_scene3() {
+			
+            // Add the light            
+			light = new THREE.PointLight(0xffffff, 0.9, 0);
+			light.position.set(0, 160, 40);
+			scene.add(light);
+
+			// Create a grid for the floor
+			var size = 200,
+				step = 20;
+
+            // Draw the plane
+			var planeGeometry = new THREE.PlaneGeometry(size * 2, size * 2);
+			var planeMaterial = new THREE.MeshPhongMaterial({
+				color: 0x444444,
+				emissive: 0x000000,
+				specular: 0x111111,
+				side: THREE.DoubleSide,
+				transparent: true,
+				opacity: 0.6
+			});
+			var plane = new THREE.Mesh(planeGeometry, planeMaterial);
+			plane.position.set(0, 0, 0);
+			plane.rotation.set(math.pi / 2, 0, 0);
+			scene.add(plane);
+
+            //Draw the lines
+			var lineGeometry = new THREE.Geometry();
+			var lineMaterial = new THREE.LineBasicMaterial({
+				color: 0x555555,
+				linewidth: 1.2
+			});
+			for (var i = -size; i <= size; i += step) {
+				lineGeometry.vertices.push(new THREE.Vector3(-size, -0.04, i));
+				lineGeometry.vertices.push(new THREE.Vector3(size, -0.04, i));
+				lineGeometry.vertices.push(new THREE.Vector3(i, -0.04, -size));
+				lineGeometry.vertices.push(new THREE.Vector3(i, -0.04, size));
+			}
+
+			var line = new THREE.LineSegments(lineGeometry, lineMaterial);
+			scene.add(line);
+		}
+
+        function add_basic_lights() {
+            // Add the light            
+			var light = new THREE.HemisphereLight(0xffffff, 0x000000, 0.8);            
+            scene.add(light);
+        }
+
+        function add_floor_theo() {
+            var textureLoader = new THREE.TextureLoader();
+
+            var texture = textureLoader.load('theo_floor1.jpg', function(tt) {    
+            });
+
+            texture.wrapS = THREE.RepeatWrapping;
+            texture.wrapT = THREE.RepeatWrapping;
+            texture.repeat = new THREE.Vector2(1,1.66);
+
+
+            var material = new THREE.MeshPhongMaterial({
+                color: 0xffffff,
+                specular: 0xffffff,
+                shininess: 100,
+                map: texture,
+
+            });
+            var geometry = new THREE.CubeGeometry(300, 300, 5);
+
+            var mesh = new THREE.Mesh(geometry, material);
+            mesh.position.y = -2;
+            mesh.rotation.x = -Math.PI / 2;
+            scene.add(mesh);
+        }
+
+        function init(cz) {
+            scene = new THREE.Scene();
+            camera = new THREE.PerspectiveCamera(30, window.innerWidth / window.innerHeight, 1, 4000);
+            camera.position.set(0, 80, 0);
+            camera.position.z = cz;
+            scene.add(camera);
+
+            // set_the_scene3();
+        }
+
+        function loadBVHFile(bvhURL, cname, jm, bm, scale) {            
+            var c2 = new BVHCharacter(cname, jm, bm, makeJointGeometry_Dode, makeBoneGeometry_Cylinder2);
+            // c2.setOriginPosition(0, 0, 0);
+            c2.skelScale = scale;
+            c2.loadFromURL(bvhURL, function() {
+                scene.add(c2.skeleton);
+            });
+
+            characters.push(c2);
+        }
+
+        function loadPosFile(csvURL, cname, jm, bm, scale, fr) {            
+            var c2 = new C3DCharacter(cname, markermaterial, makeJointGeometry_Sphere2);
+            // c2.setOriginPosition(0, 0, 0);
+            c2.scale = scale;
+            c2.frameTime = fr;
+            c2.loadFromURL(csvURL, function() {});
+
+            characters.push(c2);
+        }
+
+        function initRenderer() {
+            renderer = new THREE.WebGLRenderer({
+                antialias: true
+            });
+            renderer.setSize(window.innerWidth, window.innerHeight);
+            //renderer.setSize(600, 500);
+            renderer.gammaInput = true;
+            renderer.gammaOutput = true;
+            renderer.setPixelRatio(window.devicePixelRatio);
+            // renderer.setClearColor(0xdddddd, 1);
+            renderer.setClearColor(0xffffff, 1);
+
+            
+            document.body.appendChild(renderer.domElement);
+
+           controls = new THREE.OrbitControls(camera, renderer.domElement);
+        }
+
+        function animate() {
+            // if (!playing)
+            //     return;
+
+            requestAnimationFrame(animate);
+
+            characters.forEach(function(c) {
+                // console.log(c.playing);
+                if (1 && c.ready) {
+                    if (c.playing) {
+                        c.animIndex = c.animOffset + Math.floor((Date.now() - c.animStartTimeRef) / c.frameTime / 1000);
+
+                        if (c.animIndex >= c.frameCount) {
+                            c.animOffset = 0;
+                            c.animStartTimeRef = Date.now();
+                            c.animIndex = 0;
+                            played_count++;
+                            // if (played_count> 1)
+                            //     c.playing = false;
+
+                        }
+                        c.animFrame(c.animIndex);
+                    }
+                }
+            });
+
+            renderer.render(scene, camera);
+        }
+
+
+        $(document).ready(function () {            
+            var dataURL = getUrlParameter('data_url');                 
+            var cz = getUrlParameter('cz');
+            var scale = getUrlParameter('scale');
+            var frameTime = getUrlParameter('frame_time');
+            
+            if (cz === undefined)
+                cz = 550;
+            
+            if (scale === undefined)
+                scale = 1.0;
+            
+            if (frameTime === undefined)
+                frameTime = 1/120;
+            
+            init(cz);
+            add_basic_lights();
+            add_floor_theo();
+            initRenderer();
+            
+            if (dataURL !==  undefined) {                                
+                console.log("Loading " + dataURL);
+                
+                if (dataURL.endsWith('.bvh'))             
+                    loadBVHFile(dataURL, 'Fig', jm_a, bm_a, scale);
+                else if (dataURL.endsWith('.csv'))             
+                    loadPosFile(dataURL, 'Fig', jm_a, bm_a, scale, frameTime);
+                else
+                    console.error('Unsupport file.');
+            }
+            
+            
+
+            animate();
+        });
+
+        
+    </script>
+</body>
+
+</html>

dataloaders/pymo/mocapplayer/styles/pace.css

@@ -0,0 +1,76 @@
+.pace {
+  -webkit-pointer-events: none;
+  pointer-events: none;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  user-select: none;
+}
+
+.pace-inactive {
+  display: none;
+}
+
+.pace .pace-progress {
+  background: #29d;
+  position: fixed;
+  z-index: 2000;
+  top: 0;
+  right: 100%;
+  width: 100%;
+  height: 2px;
+}
+
+.pace .pace-progress-inner {
+  display: block;
+  position: absolute;
+  right: 0px;
+  width: 100px;
+  height: 100%;
+  box-shadow: 0 0 10px #29d, 0 0 5px #29d;
+  opacity: 1.0;
+  -webkit-transform: rotate(3deg) translate(0px, -4px);
+  -moz-transform: rotate(3deg) translate(0px, -4px);
+  -ms-transform: rotate(3deg) translate(0px, -4px);
+  -o-transform: rotate(3deg) translate(0px, -4px);
+  transform: rotate(3deg) translate(0px, -4px);
+}
+
+.pace .pace-activity {
+  display: block;
+  position: fixed;
+  z-index: 2000;
+  top: 15px;
+  right: 20px;
+  width: 34px;
+  height: 34px;
+  border: solid 2px transparent;
+  border-top-color: #9ea7ac;
+  border-left-color: #9ea7ac;
+  border-radius: 30px;
+  -webkit-animation: pace-spinner 700ms linear infinite;
+  -moz-animation: pace-spinner 700ms linear infinite;
+  -ms-animation: pace-spinner 700ms linear infinite;
+  -o-animation: pace-spinner 700ms linear infinite;
+  animation: pace-spinner 700ms linear infinite;
+}
+
+@-webkit-keyframes pace-spinner {
+  0% { -webkit-transform: rotate(0deg); transform: rotate(0deg); }
+  100% { -webkit-transform: rotate(360deg); transform: rotate(360deg); }
+}
+@-moz-keyframes pace-spinner {
+  0% { -moz-transform: rotate(0deg); transform: rotate(0deg); }
+  100% { -moz-transform: rotate(360deg); transform: rotate(360deg); }
+}
+@-o-keyframes pace-spinner {
+  0% { -o-transform: rotate(0deg); transform: rotate(0deg); }
+  100% { -o-transform: rotate(360deg); transform: rotate(360deg); }
+}
+@-ms-keyframes pace-spinner {
+  0% { -ms-transform: rotate(0deg); transform: rotate(0deg); }
+  100% { -ms-transform: rotate(360deg); transform: rotate(360deg); }
+}
+@keyframes pace-spinner {
+  0% { transform: rotate(0deg); transform: rotate(0deg); }
+  100% { transform: rotate(360deg); transform: rotate(360deg); }
+}

dataloaders/pymo/parsers.py

@@ -0,0 +1,274 @@
+'''
+BVH Parser Class
+
+By Omid Alemi
+Created: June 12, 2017
+
+Based on: https://gist.github.com/johnfredcee/2007503
+
+'''
+import re
+from unicodedata import name
+import numpy as np
+from .data import Joint, MocapData
+
+class BVHScanner():
+    '''
+    A wrapper class for re.Scanner
+    '''
+    def __init__(self):
+
+        def identifier(scanner, token):
+            return 'IDENT', token
+
+        def operator(scanner, token):
+            return 'OPERATOR', token
+
+        def digit(scanner, token):
+            return 'DIGIT', token
+
+        def open_brace(scanner, token):
+            return 'OPEN_BRACE', token
+
+        def close_brace(scanner, token):
+            return 'CLOSE_BRACE', token
+
+        self.scanner = re.Scanner([
+            (r'[a-zA-Z_]\w*', identifier),
+            #(r'-*[0-9]+(\.[0-9]+)?', digit), # won't work for .34
+            #(r'[-+]?[0-9]*\.?[0-9]+', digit), # won't work for 4.56e-2
+            #(r'[-+]?[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?', digit),
+            (r'-*[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?', digit),
+            (r'}', close_brace),
+            (r'}', close_brace),
+            (r'{', open_brace),
+            (r':', None),
+            (r'\s+', None)
+        ])
+
+    def scan(self, stuff):
+        return self.scanner.scan(stuff)
+
+
+
+class BVHParser():
+    '''
+    A class to parse a BVH file.
+    
+    Extracts the skeleton and channel values
+    '''
+    def __init__(self, filename=None):
+        self.reset()
+
+    def reset(self): 
+        self._skeleton = {}
+        self.bone_context = []
+        self._motion_channels = []
+        self._motions = []
+        self.current_token = 0
+        self.framerate = 0.0
+        self.root_name = ''
+        
+        self.scanner = BVHScanner()
+        
+        self.data = MocapData()
+
+
+    def parse(self, filename, start=0, stop=-1):
+        self.reset()
+        self.correct_row_num = 0
+        with open(filename, 'r') as f:
+            for line in f.readlines():
+                self.correct_row_num += 1
+
+        with open(filename, 'r') as bvh_file:
+            raw_contents = bvh_file.read()
+        tokens, remainder = self.scanner.scan(raw_contents)
+        
+        self._parse_hierarchy(tokens)
+        self.current_token = self.current_token + 1
+        self._parse_motion(tokens, start, stop)
+        
+        self.data.skeleton = self._skeleton
+        self.data.channel_names = self._motion_channels
+        self.data.values = self._to_DataFrame()
+        self.data.root_name = self.root_name
+        self.data.framerate = self.framerate
+
+        return self.data
+    
+    def _to_DataFrame(self):
+        '''Returns all of the channels parsed from the file as a pandas DataFrame'''
+
+        import pandas as pd
+        time_index = pd.to_timedelta([f[0] for f in self._motions], unit='s')
+        frames = [f[1] for f in self._motions]
+        channels = np.asarray([[channel[2] for channel in frame] for frame in frames])
+        column_names = ['%s_%s'%(c[0], c[1]) for c in self._motion_channels]
+
+        return pd.DataFrame(data=channels, index=time_index, columns=column_names)
+
+
+    def _new_bone(self, parent, name):
+        bone = {'parent': parent, 'channels': [], 'offsets': [], 'order': '','children': []}
+        return bone
+
+    def _push_bone_context(self,name):
+        self.bone_context.append(name)
+
+    def _get_bone_context(self):
+        return self.bone_context[len(self.bone_context)-1]
+
+    def _pop_bone_context(self):
+        self.bone_context = self.bone_context[:-1]
+        return self.bone_context[len(self.bone_context)-1]
+
+    def _read_offset(self, bvh, token_index):
+        if bvh[token_index] != ('IDENT', 'OFFSET'):
+            return None, None
+        token_index = token_index + 1
+        offsets = [0.0] * 3
+        for i in range(3):
+            offsets[i] = float(bvh[token_index][1])
+            token_index = token_index + 1
+        return offsets, token_index
+    
+    def _read_channels(self, bvh, token_index):
+        if bvh[token_index] != ('IDENT', 'CHANNELS'):
+            return None, None
+        token_index = token_index + 1
+        channel_count = int(bvh[token_index][1])
+        token_index = token_index + 1
+        channels = [""] * channel_count
+        order = ""
+        for i in range(channel_count):
+            channels[i] = bvh[token_index][1]
+            token_index = token_index + 1
+            if(channels[i] == "Xrotation" or channels[i]== "Yrotation" or channels[i]== "Zrotation"):
+                order += channels[i][0]
+            else :
+                order = ""
+        return channels, token_index, order
+
+    def _parse_joint(self, bvh, token_index):
+        end_site = False
+        joint_id = bvh[token_index][1]
+        token_index = token_index + 1
+        joint_name = bvh[token_index][1]
+        token_index = token_index + 1
+        
+        parent_name = self._get_bone_context()
+
+        if (joint_id == "End"):
+            joint_name = parent_name+ '_Nub'
+            end_site = True
+        joint = self._new_bone(parent_name, joint_name)
+        if bvh[token_index][0] != 'OPEN_BRACE':
+            print('Was expecting brance, got ', bvh[token_index])
+            return None
+        token_index = token_index + 1
+        offsets, token_index = self._read_offset(bvh, token_index)
+        joint['offsets'] = offsets
+        if not end_site:
+            channels, token_index, order = self._read_channels(bvh, token_index)
+            joint['channels'] = channels
+            joint['order'] = order
+            for channel in channels:
+                self._motion_channels.append((joint_name, channel))
+
+        self._skeleton[joint_name] = joint
+        self._skeleton[parent_name]['children'].append(joint_name)
+
+        while (bvh[token_index][0] == 'IDENT' and bvh[token_index][1] == 'JOINT') or  (bvh[token_index][0] == 'IDENT' and bvh[token_index][1] == 'End'):
+            self._push_bone_context(joint_name)
+            token_index = self._parse_joint(bvh, token_index)
+            self._pop_bone_context()
+
+        if bvh[token_index][0] == 'CLOSE_BRACE':
+            return token_index + 1
+
+        print('Unexpected token ', bvh[token_index])
+
+    def _parse_hierarchy(self, bvh):
+        self.current_token = 0
+        if bvh[self.current_token] != ('IDENT', 'HIERARCHY'):
+            return None
+        self.current_token = self.current_token + 1
+        if bvh[self.current_token] != ('IDENT', 'ROOT'):
+            return None
+        self.current_token = self.current_token + 1
+        if bvh[self.current_token][0] != 'IDENT':
+            return None
+
+        root_name = bvh[self.current_token][1]
+        root_bone = self._new_bone(None, root_name)
+        self.current_token = self.current_token + 2 #skipping open brace
+        offsets, self.current_token = self._read_offset(bvh, self.current_token)
+        channels, self.current_token, order = self._read_channels(bvh, self.current_token)
+        root_bone['offsets'] = offsets
+        root_bone['channels'] = channels
+        root_bone['order'] = order
+        self._skeleton[root_name] = root_bone
+        self._push_bone_context(root_name)
+        
+        for channel in channels:
+            self._motion_channels.append((root_name, channel))
+
+        while bvh[self.current_token][1] == 'JOINT':
+            self.current_token = self._parse_joint(bvh, self.current_token)
+        
+        self.root_name = root_name
+
+    def _parse_motion(self, bvh, start, stop):
+        if bvh[self.current_token][0] != 'IDENT':
+            print('Unexpected text')
+            return None
+        if bvh[self.current_token][1] != 'MOTION':
+            print('No motion section')
+            return None
+        self.current_token = self.current_token + 1
+        if bvh[self.current_token][1] != 'Frames':
+            return None
+        self.current_token = self.current_token + 1
+        frame_count = int(bvh[self.current_token][1])
+        
+        if stop<0 or stop>frame_count:
+            stop = min(frame_count, self.correct_row_num-431)
+            
+        assert(start>=0)
+        assert(start<stop)
+        
+        self.current_token = self.current_token + 1
+        if bvh[self.current_token][1] != 'Frame':
+            return None
+        self.current_token = self.current_token + 1
+        if bvh[self.current_token][1] != 'Time':
+            return None
+        self.current_token = self.current_token + 1
+        frame_rate = float(bvh[self.current_token][1])
+
+        self.framerate = frame_rate
+       
+        self.current_token = self.current_token + 1
+       
+        frame_time = 0.0
+        self._motions = [()] * (stop-start)
+        idx=0
+        for i in range(stop):
+            #print(i)
+            channel_values = []
+            
+            for channel in self._motion_channels:
+                #print(channel)
+                channel_values.append((channel[0], channel[1], float(bvh[self.current_token][1])))
+                self.current_token = self.current_token + 1
+
+            if i>=start:
+                self._motions[idx] = (frame_time, channel_values)
+                frame_time = frame_time + frame_rate
+                idx+=1
+
+
+if __name__ == "__main__":
+    p = BVHParser()
+    data = [p.parse("../../../datasets/beat_full/2/2_scott_0_1_1.bvh")]

dataloaders/pymo/preprocessing.py

@@ -0,0 +1,726 @@
+'''
+Preprocessing Tranformers Based on sci-kit's API
+
+By Omid Alemi
+Created on June 12, 2017
+'''
+import copy
+import pandas as pd
+import numpy as np
+from sklearn.base import BaseEstimator, TransformerMixin
+from .Quaternions import Quaternions
+from .rotation_tools import Rotation
+
+class MocapParameterizer(BaseEstimator, TransformerMixin):
+    def __init__(self, param_type = 'euler'):
+        '''
+        
+        param_type = {'euler', 'quat', 'expmap', 'position'}
+        '''
+        self.param_type = param_type
+
+    def fit(self, X, y=None):
+        return self
+
+    def transform(self, X, y=None):
+        if self.param_type == 'euler':
+            return X
+        elif self.param_type == 'expmap':
+            return self._to_expmap(X)
+        elif self.param_type == 'quat':
+            return X
+        elif self.param_type == 'position':
+            return self._to_pos(X)
+        else:
+            raise UnsupportedParamError('Unsupported param: %s. Valid param types are: euler, quat, expmap, position' % self.param_type)
+#        return X
+    
+    def inverse_transform(self, X, copy=None): 
+        if self.param_type == 'euler':
+            return X
+        elif self.param_type == 'expmap':
+            return self._expmap_to_euler(X)
+        elif self.param_type == 'quat':
+            raise UnsupportedParamError('quat2euler is not supported')
+        elif self.param_type == 'position':
+            print('positions 2 eulers is not supported')
+            return X
+        else:
+            raise UnsupportedParamError('Unsupported param: %s. Valid param types are: euler, quat, expmap, position' % self.param_type)
+
+    def _to_pos(self, X):
+        '''Converts joints rotations in Euler angles to joint positions'''
+
+        Q = []
+        for track in X:
+            channels = []
+            titles = []
+            euler_df = track.values
+
+            # Create a new DataFrame to store the exponential map rep
+            pos_df = pd.DataFrame(index=euler_df.index)
+
+            # Copy the root rotations into the new DataFrame
+            # rxp = '%s_Xrotation'%track.root_name
+            # ryp = '%s_Yrotation'%track.root_name
+            # rzp = '%s_Zrotation'%track.root_name
+            # pos_df[rxp] = pd.Series(data=euler_df[rxp], index=pos_df.index)
+            # pos_df[ryp] = pd.Series(data=euler_df[ryp], index=pos_df.index)
+            # pos_df[rzp] = pd.Series(data=euler_df[rzp], index=pos_df.index)
+
+            # List the columns that contain rotation channels
+            rot_cols = [c for c in euler_df.columns if ('rotation' in c)]
+
+            # List the columns that contain position channels
+            pos_cols = [c for c in euler_df.columns if ('position' in c)]
+
+            # List the joints that are not end sites, i.e., have channels
+            joints = (joint for joint in track.skeleton)
+            
+            tree_data = {}
+
+            for joint in track.traverse():
+                parent = track.skeleton[joint]['parent']
+                rot_order = track.skeleton[joint]['order']
+                #print("rot_order:" + joint + " :" + rot_order)
+
+                # Get the rotation columns that belong to this joint
+                rc = euler_df[[c for c in rot_cols if joint in c]]
+
+                # Get the position columns that belong to this joint
+                pc = euler_df[[c for c in pos_cols if joint in c]]
+
+                # Make sure the columns are organized in xyz order
+                if rc.shape[1] < 3:
+                    euler_values = np.zeros((euler_df.shape[0], 3))
+                    rot_order = "XYZ"
+                else:
+                    euler_values = np.pi/180.0*np.transpose(np.array([track.values['%s_%srotation'%(joint, rot_order[0])], track.values['%s_%srotation'%(joint, rot_order[1])], track.values['%s_%srotation'%(joint, rot_order[2])]]))
+
+                if pc.shape[1] < 3:
+                    pos_values = np.asarray([[0,0,0] for f in pc.iterrows()])
+                else:
+                    pos_values =np.asarray([[f[1]['%s_Xposition'%joint], 
+                                  f[1]['%s_Yposition'%joint], 
+                                  f[1]['%s_Zposition'%joint]] for f in pc.iterrows()])
+                
+                quats = Quaternions.from_euler(np.asarray(euler_values), order=rot_order.lower(), world=False)
+                
+                tree_data[joint]=[
+                                    [], # to store the rotation matrix
+                                    []  # to store the calculated position
+                                 ] 
+                if track.root_name == joint:
+                    tree_data[joint][0] = quats#rotmats
+                    # tree_data[joint][1] = np.add(pos_values, track.skeleton[joint]['offsets'])
+                    tree_data[joint][1] = pos_values
+                else:
+                    # for every frame i, multiply this joint's rotmat to the rotmat of its parent
+                    tree_data[joint][0] = tree_data[parent][0]*quats# np.matmul(rotmats, tree_data[parent][0])
+
+                    # add the position channel to the offset and store it in k, for every frame i
+                    k = pos_values + np.asarray(track.skeleton[joint]['offsets'])
+
+                    # multiply k to the rotmat of the parent for every frame i
+                    q = tree_data[parent][0]*k #np.matmul(k.reshape(k.shape[0],1,3), tree_data[parent][0])
+
+                    # add q to the position of the parent, for every frame i
+                    tree_data[joint][1] = tree_data[parent][1] + q #q.reshape(k.shape[0],3) + tree_data[parent][1]
+
+                # Create the corresponding columns in the new DataFrame
+                pos_df['%s_Xposition'%joint] = pd.Series(data=[e[0] for e in tree_data[joint][1]], index=pos_df.index)
+                pos_df['%s_Yposition'%joint] = pd.Series(data=[e[1] for e in tree_data[joint][1]], index=pos_df.index)
+                pos_df['%s_Zposition'%joint] = pd.Series(data=[e[2] for e in tree_data[joint][1]], index=pos_df.index)
+
+
+            new_track = track.clone()
+            new_track.values = pos_df
+            Q.append(new_track)
+        return Q
+
+
+    def _to_expmap(self, X):
+        '''Converts Euler angles to Exponential Maps'''
+
+        Q = []
+        for track in X:
+            channels = []
+            titles = []
+            euler_df = track.values
+
+            # Create a new DataFrame to store the exponential map rep
+            exp_df = pd.DataFrame(index=euler_df.index)
+
+            # Copy the root positions into the new DataFrame
+            rxp = '%s_Xposition'%track.root_name
+            ryp = '%s_Yposition'%track.root_name
+            rzp = '%s_Zposition'%track.root_name
+            exp_df[rxp] = pd.Series(data=euler_df[rxp], index=exp_df.index)
+            exp_df[ryp] = pd.Series(data=euler_df[ryp], index=exp_df.index)
+            exp_df[rzp] = pd.Series(data=euler_df[rzp], index=exp_df.index)
+            
+            # List the columns that contain rotation channels
+            rots = [c for c in euler_df.columns if ('rotation' in c and 'Nub' not in c)]
+
+            # List the joints that are not end sites, i.e., have channels
+            joints = (joint for joint in track.skeleton if 'Nub' not in joint)
+
+            for joint in joints:
+                r = euler_df[[c for c in rots if joint in c]] # Get the columns that belong to this joint
+                euler = [[f[1]['%s_Xrotation'%joint], f[1]['%s_Yrotation'%joint], f[1]['%s_Zrotation'%joint]] for f in r.iterrows()] # Make sure the columsn are organized in xyz order
+                exps = [Rotation(f, 'euler', from_deg=True).to_expmap() for f in euler] # Convert the eulers to exp maps
+                
+                # Create the corresponding columns in the new DataFrame
+    
+                exp_df['%s_alpha'%joint] = pd.Series(data=[e[0] for e in exps], index=exp_df.index)
+                exp_df['%s_beta'%joint] = pd.Series(data=[e[1] for e in exps], index=exp_df.index)
+                exp_df['%s_gamma'%joint] = pd.Series(data=[e[2] for e in exps], index=exp_df.index)
+
+            new_track = track.clone()
+            new_track.values = exp_df
+            Q.append(new_track)
+
+        return Q
+
+    def _expmap_to_euler(self, X):
+        Q = []
+        for track in X:
+            channels = []
+            titles = []
+            exp_df = track.values
+
+            # Create a new DataFrame to store the exponential map rep
+            euler_df = pd.DataFrame(index=exp_df.index)
+
+            # Copy the root positions into the new DataFrame
+            rxp = '%s_Xposition'%track.root_name
+            ryp = '%s_Yposition'%track.root_name
+            rzp = '%s_Zposition'%track.root_name
+            euler_df[rxp] = pd.Series(data=exp_df[rxp], index=euler_df.index)
+            euler_df[ryp] = pd.Series(data=exp_df[ryp], index=euler_df.index)
+            euler_df[rzp] = pd.Series(data=exp_df[rzp], index=euler_df.index)
+            
+            # List the columns that contain rotation channels
+            exp_params = [c for c in exp_df.columns if ( any(p in c for p in ['alpha', 'beta','gamma']) and 'Nub' not in c)]
+
+            # List the joints that are not end sites, i.e., have channels
+            joints = (joint for joint in track.skeleton if 'Nub' not in joint)
+
+            for joint in joints:
+                r = exp_df[[c for c in exp_params if joint in c]] # Get the columns that belong to this joint
+                expmap = [[f[1]['%s_alpha'%joint], f[1]['%s_beta'%joint], f[1]['%s_gamma'%joint]] for f in r.iterrows()] # Make sure the columsn are organized in xyz order
+                euler_rots = [Rotation(f, 'expmap').to_euler(True)[0] for f in expmap] # Convert the eulers to exp maps
+                
+                # Create the corresponding columns in the new DataFrame
+    
+                euler_df['%s_Xrotation'%joint] = pd.Series(data=[e[0] for e in euler_rots], index=euler_df.index)
+                euler_df['%s_Yrotation'%joint] = pd.Series(data=[e[1] for e in euler_rots], index=euler_df.index)
+                euler_df['%s_Zrotation'%joint] = pd.Series(data=[e[2] for e in euler_rots], index=euler_df.index)
+
+            new_track = track.clone()
+            new_track.values = euler_df
+            Q.append(new_track)
+
+        return Q
+
+
+class JointSelector(BaseEstimator, TransformerMixin):
+    '''
+    Allows for filtering the mocap data to include only the selected joints
+    '''
+    def __init__(self, joints, include_root=False):
+        self.joints = joints
+        self.include_root = include_root
+
+    def fit(self, X, y=None):
+        return self
+
+    def transform(self, X, y=None):
+        selected_joints = []
+        selected_channels = []
+
+        if self.include_root:
+            selected_joints.append(X[0].root_name)
+        
+        selected_joints.extend(self.joints)
+
+        for joint_name in selected_joints:
+            selected_channels.extend([o for o in X[0].values.columns if joint_name in o])
+            
+        Q = []
+
+
+        for track in X:
+            t2 = track.clone()
+            
+            for key in track.skeleton.keys():
+                if key not in selected_joints:
+                    t2.skeleton.pop(key)
+            t2.values = track.values[selected_channels]
+
+            Q.append(t2)
+      
+
+        return Q
+
+
+class Numpyfier(BaseEstimator, TransformerMixin):
+    '''
+    Just converts the values in a MocapData object into a numpy array
+    Useful for the final stage of a pipeline before training
+    '''
+    def __init__(self):
+        pass
+
+    def fit(self, X, y=None):
+        self.org_mocap_ = X[0].clone()
+        self.org_mocap_.values.drop(self.org_mocap_.values.index, inplace=True)
+
+        return self
+
+    def transform(self, X, y=None):
+        Q = []
+        
+        for track in X:
+            Q.append(track.values.values)
+
+        return np.array(Q)
+
+    def inverse_transform(self, X, copy=None):
+        Q = []
+
+        for track in X:
+            
+            new_mocap = self.org_mocap_.clone()
+            time_index = pd.to_timedelta([f for f in range(track.shape[0])], unit='s')
+
+            new_df =  pd.DataFrame(data=track, index=time_index, columns=self.org_mocap_.values.columns)
+            
+            new_mocap.values = new_df
+            
+
+            Q.append(new_mocap)
+
+        return Q
+
+class RootTransformer(BaseEstimator, TransformerMixin):
+    def __init__(self, method):
+        """
+        Accepted methods:
+            abdolute_translation_deltas
+            pos_rot_deltas
+        """
+        self.method = method
+    
+    def fit(self, X, y=None):
+        return self
+    
+    def transform(self, X, y=None):
+        Q = []
+
+        for track in X:
+            if self.method == 'abdolute_translation_deltas':
+                new_df = track.values.copy()
+                xpcol = '%s_Xposition'%track.root_name
+                ypcol = '%s_Yposition'%track.root_name
+                zpcol = '%s_Zposition'%track.root_name
+
+
+                dxpcol = '%s_dXposition'%track.root_name
+                dzpcol = '%s_dZposition'%track.root_name
+
+                dx = track.values[xpcol].diff()
+                dz = track.values[zpcol].diff()    
+
+                dx[0] = 0
+                dz[0] = 0
+
+                new_df.drop([xpcol, zpcol], axis=1, inplace=True)
+
+                new_df[dxpcol] = dx
+                new_df[dzpcol] = dz
+                
+                new_track = track.clone()
+                new_track.values = new_df
+            # end of abdolute_translation_deltas
+            
+            elif self.method == 'pos_rot_deltas':
+                new_track = track.clone()
+
+                # Absolute columns
+                xp_col = '%s_Xposition'%track.root_name
+                yp_col = '%s_Yposition'%track.root_name
+                zp_col = '%s_Zposition'%track.root_name
+
+                xr_col = '%s_Xrotation'%track.root_name
+                yr_col = '%s_Yrotation'%track.root_name
+                zr_col = '%s_Zrotation'%track.root_name
+
+                # Delta columns
+                dxp_col = '%s_dXposition'%track.root_name
+                dzp_col = '%s_dZposition'%track.root_name
+
+                dxr_col = '%s_dXrotation'%track.root_name
+                dyr_col = '%s_dYrotation'%track.root_name
+                dzr_col = '%s_dZrotation'%track.root_name
+
+
+                new_df = track.values.copy()
+
+                root_pos_x_diff = pd.Series(data=track.values[xp_col].diff(), index=new_df.index)
+                root_pos_z_diff = pd.Series(data=track.values[zp_col].diff(), index=new_df.index)
+
+                root_rot_y_diff = pd.Series(data=track.values[yr_col].diff(), index=new_df.index)
+                root_rot_x_diff = pd.Series(data=track.values[xr_col].diff(), index=new_df.index)
+                root_rot_z_diff = pd.Series(data=track.values[zr_col].diff(), index=new_df.index)
+
+
+                root_pos_x_diff[0] = 0
+                root_pos_z_diff[0] = 0
+
+                root_rot_y_diff[0] = 0
+                root_rot_x_diff[0] = 0
+                root_rot_z_diff[0] = 0
+
+                new_df.drop([xr_col, yr_col, zr_col, xp_col, zp_col], axis=1, inplace=True)
+
+                new_df[dxp_col] = root_pos_x_diff
+                new_df[dzp_col] = root_pos_z_diff
+
+                new_df[dxr_col] = root_rot_x_diff
+                new_df[dyr_col] = root_rot_y_diff
+                new_df[dzr_col] = root_rot_z_diff
+
+                new_track.values = new_df
+
+            Q.append(new_track)
+
+        return Q
+
+    def inverse_transform(self, X, copy=None, start_pos=None):
+        Q = []
+
+        #TODO: simplify this implementation
+
+        startx = 0
+        startz = 0
+
+        if start_pos is not None:
+            startx, startz = start_pos
+
+        for track in X:
+            new_track = track.clone()
+            if self.method == 'abdolute_translation_deltas':
+                new_df = new_track.values
+                xpcol = '%s_Xposition'%track.root_name
+                ypcol = '%s_Yposition'%track.root_name
+                zpcol = '%s_Zposition'%track.root_name
+
+
+                dxpcol = '%s_dXposition'%track.root_name
+                dzpcol = '%s_dZposition'%track.root_name
+
+                dx = track.values[dxpcol].values
+                dz = track.values[dzpcol].values
+
+                recx = [startx]
+                recz = [startz]
+
+                for i in range(dx.shape[0]-1):
+                    recx.append(recx[i]+dx[i+1])
+                    recz.append(recz[i]+dz[i+1])
+
+                # recx = [recx[i]+dx[i+1] for i in range(dx.shape[0]-1)]
+                # recz = [recz[i]+dz[i+1] for i in range(dz.shape[0]-1)]
+                # recx = dx[:-1] + dx[1:]
+                # recz = dz[:-1] + dz[1:]
+
+                new_df[xpcol] = pd.Series(data=recx, index=new_df.index)
+                new_df[zpcol] = pd.Series(data=recz, index=new_df.index)
+
+                new_df.drop([dxpcol, dzpcol], axis=1, inplace=True)
+                
+                new_track.values = new_df
+            # end of abdolute_translation_deltas
+            
+            elif self.method == 'pos_rot_deltas':
+                new_track = track.clone()
+
+                # Absolute columns
+                xp_col = '%s_Xposition'%track.root_name
+                yp_col = '%s_Yposition'%track.root_name
+                zp_col = '%s_Zposition'%track.root_name
+
+                xr_col = '%s_Xrotation'%track.root_name
+                yr_col = '%s_Yrotation'%track.root_name
+                zr_col = '%s_Zrotation'%track.root_name
+
+                # Delta columns
+                dxp_col = '%s_dXposition'%track.root_name
+                dzp_col = '%s_dZposition'%track.root_name
+
+                dxr_col = '%s_dXrotation'%track.root_name
+                dyr_col = '%s_dYrotation'%track.root_name
+                dzr_col = '%s_dZrotation'%track.root_name
+
+
+                new_df = track.values.copy()
+
+                dx = track.values[dxp_col].values
+                dz = track.values[dzp_col].values
+
+                drx = track.values[dxr_col].values
+                dry = track.values[dyr_col].values
+                drz = track.values[dzr_col].values
+
+                rec_xp = [startx]
+                rec_zp = [startz]
+
+                rec_xr = [0]
+                rec_yr = [0]
+                rec_zr = [0]
+
+
+                for i in range(dx.shape[0]-1):
+                    rec_xp.append(rec_xp[i]+dx[i+1])
+                    rec_zp.append(rec_zp[i]+dz[i+1])
+                    
+                    rec_xr.append(rec_xr[i]+drx[i+1])
+                    rec_yr.append(rec_yr[i]+dry[i+1])
+                    rec_zr.append(rec_zr[i]+drz[i+1])
+
+
+                new_df[xp_col] = pd.Series(data=rec_xp, index=new_df.index)
+                new_df[zp_col] = pd.Series(data=rec_zp, index=new_df.index)
+
+                new_df[xr_col] = pd.Series(data=rec_xr, index=new_df.index)
+                new_df[yr_col] = pd.Series(data=rec_yr, index=new_df.index)
+                new_df[zr_col] = pd.Series(data=rec_zr, index=new_df.index)
+
+                new_df.drop([dxr_col, dyr_col, dzr_col, dxp_col, dzp_col], axis=1, inplace=True)
+
+
+                new_track.values = new_df
+
+            Q.append(new_track)
+
+        return Q
+
+
+class RootCentricPositionNormalizer(BaseEstimator, TransformerMixin):
+    def __init__(self):
+        pass
+
+    def fit(self, X, y=None):
+        return self
+
+    def transform(self, X, y=None):
+        Q = []
+
+        for track in X:
+            new_track = track.clone()
+
+            rxp = '%s_Xposition'%track.root_name
+            ryp = '%s_Yposition'%track.root_name
+            rzp = '%s_Zposition'%track.root_name
+
+            projected_root_pos = track.values[[rxp, ryp, rzp]]
+
+            projected_root_pos.loc[:,ryp] = 0 # we want the root's projection on the floor plane as the ref
+
+            new_df = pd.DataFrame(index=track.values.index)
+
+            all_but_root = [joint for joint in track.skeleton if track.root_name not in joint]
+            # all_but_root = [joint for joint in track.skeleton]
+            for joint in all_but_root:                
+                new_df['%s_Xposition'%joint] = pd.Series(data=track.values['%s_Xposition'%joint]-projected_root_pos[rxp], index=new_df.index)
+                new_df['%s_Yposition'%joint] = pd.Series(data=track.values['%s_Yposition'%joint]-projected_root_pos[ryp], index=new_df.index)
+                new_df['%s_Zposition'%joint] = pd.Series(data=track.values['%s_Zposition'%joint]-projected_root_pos[rzp], index=new_df.index)
+            
+
+            # keep the root as it is now
+            new_df[rxp] = track.values[rxp]
+            new_df[ryp] = track.values[ryp]
+            new_df[rzp] = track.values[rzp]
+
+            new_track.values = new_df
+
+            Q.append(new_track)
+        
+        return Q
+
+    def inverse_transform(self, X, copy=None):
+        Q = []
+
+        for track in X:
+            new_track = track.clone()
+
+            rxp = '%s_Xposition'%track.root_name
+            ryp = '%s_Yposition'%track.root_name
+            rzp = '%s_Zposition'%track.root_name
+
+            projected_root_pos = track.values[[rxp, ryp, rzp]]
+
+            projected_root_pos.loc[:,ryp] = 0 # we want the root's projection on the floor plane as the ref
+
+            new_df = pd.DataFrame(index=track.values.index)
+
+            for joint in track.skeleton:                
+                new_df['%s_Xposition'%joint] = pd.Series(data=track.values['%s_Xposition'%joint]+projected_root_pos[rxp], index=new_df.index)
+                new_df['%s_Yposition'%joint] = pd.Series(data=track.values['%s_Yposition'%joint]+projected_root_pos[ryp], index=new_df.index)
+                new_df['%s_Zposition'%joint] = pd.Series(data=track.values['%s_Zposition'%joint]+projected_root_pos[rzp], index=new_df.index)
+                
+
+            new_track.values = new_df
+
+            Q.append(new_track)
+        
+        return Q
+
+
+class Flattener(BaseEstimator, TransformerMixin):
+    def __init__(self):
+        pass
+
+    def fit(self, X, y=None):
+        return self
+
+    def transform(self, X, y=None):
+        return np.concatenate(X, axis=0)
+
+class ConstantsRemover(BaseEstimator, TransformerMixin):
+    '''
+    For now it just looks at the first track
+    '''
+
+    def __init__(self, eps = 10e-10):
+        self.eps = eps
+        
+
+    def fit(self, X, y=None):
+        stds = X[0].values.std()
+        cols = X[0].values.columns.values
+        self.const_dims_ = [c for c in cols if (stds[c] < self.eps).any()]
+        self.const_values_ = {c:X[0].values[c].values[0] for c in cols if (stds[c] < self.eps).any()}
+        return self
+
+    def transform(self, X, y=None):
+        Q = []
+        
+
+        for track in X:
+            t2 = track.clone()
+            #for key in t2.skeleton.keys():
+            #    if key in self.ConstDims_:
+            #        t2.skeleton.pop(key)
+            t2.values = track.values[track.values.columns.difference(self.const_dims_)]
+            Q.append(t2)
+        
+        return Q
+    
+    def inverse_transform(self, X, copy=None):
+        Q = []
+        
+        for track in X:
+            t2 = track.clone()
+            for d in self.const_dims_:
+                t2.values[d] = self.const_values_[d]
+            Q.append(t2)
+
+        return Q
+
+class ListStandardScaler(BaseEstimator, TransformerMixin):
+    def __init__(self, is_DataFrame=False):
+        self.is_DataFrame = is_DataFrame
+    
+    def fit(self, X, y=None):
+        if self.is_DataFrame:
+            X_train_flat = np.concatenate([m.values for m in X], axis=0)
+        else:
+            X_train_flat = np.concatenate([m for m in X], axis=0)
+
+        self.data_mean_ = np.mean(X_train_flat, axis=0)
+        self.data_std_ = np.std(X_train_flat, axis=0)
+
+        return self
+    
+    def transform(self, X, y=None):
+        Q = []
+        
+        for track in X:
+            if self.is_DataFrame:
+                normalized_track = track.copy()
+                normalized_track.values = (track.values - self.data_mean_) / self.data_std_
+            else:
+                normalized_track = (track - self.data_mean_) / self.data_std_
+
+            Q.append(normalized_track)
+        
+        if self.is_DataFrame:
+            return Q
+        else:
+            return np.array(Q)
+
+    def inverse_transform(self, X, copy=None):
+        Q = []
+        
+        for track in X:
+            
+            if self.is_DataFrame:
+                unnormalized_track = track.copy()
+                unnormalized_track.values = (track.values * self.data_std_) + self.data_mean_
+            else:
+                unnormalized_track = (track * self.data_std_) + self.data_mean_
+
+            Q.append(unnormalized_track)
+        
+        if self.is_DataFrame:
+            return Q
+        else:
+            return np.array(Q)
+
+class DownSampler(BaseEstimator, TransformerMixin):
+    def __init__(self, rate):
+        self.rate = rate
+        
+    
+    def fit(self, X, y=None):    
+
+        return self
+    
+    def transform(self, X, y=None):
+        Q = []
+        
+        for track in X:
+            #print(track.values.size)
+            #new_track = track.clone()                            
+            #new_track.values = track.values[0:-1:self.rate]            
+            #print(new_track.values.size)
+            new_track = track[0:-1:self.rate]
+            Q.append(new_track)
+        
+        return Q
+        
+    def inverse_transform(self, X, copy=None):
+      return X
+
+
+#TODO: JointsSelector (x)
+#TODO: SegmentMaker
+#TODO: DynamicFeaturesAdder
+#TODO: ShapeFeaturesAdder
+#TODO: DataFrameNumpier (x)
+
+class TemplateTransform(BaseEstimator, TransformerMixin):
+    def __init__(self):
+        pass
+
+    def fit(self, X, y=None):
+        return self
+
+    def transform(self, X, y=None):
+        return X
+
+class UnsupportedParamError(Exception):
+    def __init__(self, message):
+        self.message = message

dataloaders/pymo/rotation_tools.py

@@ -0,0 +1,153 @@
+'''
+Tools for Manipulating and Converting 3D Rotations
+
+By Omid Alemi
+Created: June 12, 2017
+
+Adapted from that matlab file...
+'''
+
+import math
+import numpy as np
+
+def deg2rad(x):
+    return x/180*math.pi
+
+
+def rad2deg(x):
+    return x/math.pi*180
+
+class Rotation():
+    def __init__(self,rot, param_type, rotation_order, **params):
+        self.rotmat = []
+        self.rotation_order = rotation_order
+        if param_type == 'euler':
+            self._from_euler(rot[0],rot[1],rot[2], params)
+        elif param_type == 'expmap':
+            self._from_expmap(rot[0], rot[1], rot[2], params)
+
+    def _from_euler(self, alpha, beta, gamma, params):
+        '''Expecting degress'''
+
+        if params['from_deg']==True:
+            alpha = deg2rad(alpha)
+            beta = deg2rad(beta)
+            gamma = deg2rad(gamma)
+        
+        ca = math.cos(alpha)
+        cb = math.cos(beta)
+        cg = math.cos(gamma)
+        sa = math.sin(alpha)
+        sb = math.sin(beta)
+        sg = math.sin(gamma)        
+
+        Rx = np.asarray([[1, 0, 0], 
+              [0, ca, sa], 
+              [0, -sa, ca]
+              ])
+
+        Ry = np.asarray([[cb, 0, -sb], 
+              [0, 1, 0],
+              [sb, 0, cb]])
+
+        Rz = np.asarray([[cg, sg, 0],
+              [-sg, cg, 0],
+              [0, 0, 1]])
+
+        self.rotmat = np.eye(3)
+
+        ############################ inner product rotation matrix in order defined at BVH file #########################
+        for axis in self.rotation_order :
+            if axis == 'X' :
+                self.rotmat = np.matmul(Rx, self.rotmat)
+            elif axis == 'Y':
+                self.rotmat = np.matmul(Ry, self.rotmat)
+            else :
+                self.rotmat = np.matmul(Rz, self.rotmat)
+        ################################################################################################################
+   
+    def _from_expmap(self, alpha, beta, gamma, params):
+        if (alpha == 0 and beta == 0 and gamma == 0):
+            self.rotmat = np.eye(3)
+            return
+
+        #TODO: Check exp map params
+
+        theta = np.linalg.norm([alpha, beta, gamma])
+
+        expmap = [alpha, beta, gamma] / theta
+
+        x = expmap[0]
+        y = expmap[1]
+        z = expmap[2]
+
+        s = math.sin(theta/2)
+        c = math.cos(theta/2)
+
+        self.rotmat = np.asarray([
+            [2*(x**2-1)*s**2+1,  2*x*y*s**2-2*z*c*s,  2*x*z*s**2+2*y*c*s],
+            [2*x*y*s**2+2*z*c*s,  2*(y**2-1)*s**2+1,  2*y*z*s**2-2*x*c*s],
+            [2*x*z*s**2-2*y*c*s, 2*y*z*s**2+2*x*c*s , 2*(z**2-1)*s**2+1]
+        ])
+        
+
+
+    def get_euler_axis(self):
+        R = self.rotmat
+        theta = math.acos((self.rotmat.trace() - 1) / 2)
+        axis = np.asarray([R[2,1] - R[1,2], R[0,2] - R[2,0], R[1,0] - R[0,1]])
+        axis = axis/(2*math.sin(theta))
+        return theta, axis
+
+    def to_expmap(self):
+        theta, axis = self.get_euler_axis()
+        rot_arr = theta * axis
+        if np.isnan(rot_arr).any():
+            rot_arr = [0, 0, 0]
+        return rot_arr
+    
+    def to_euler(self, use_deg=False):
+        eulers = np.zeros((2, 3))
+
+        if np.absolute(np.absolute(self.rotmat[2, 0]) - 1) < 1e-12:
+            #GIMBAL LOCK!
+            print('Gimbal')
+            if np.absolute(self.rotmat[2, 0]) - 1 < 1e-12:
+                eulers[:,0] = math.atan2(-self.rotmat[0,1], -self.rotmat[0,2])
+                eulers[:,1] = -math.pi/2
+            else:
+                eulers[:,0] = math.atan2(self.rotmat[0,1], -elf.rotmat[0,2])
+                eulers[:,1] = math.pi/2
+            
+            return eulers
+
+        theta = - math.asin(self.rotmat[2,0])
+        theta2 = math.pi - theta
+
+        # psi1, psi2
+        eulers[0,0] = math.atan2(self.rotmat[2,1]/math.cos(theta), self.rotmat[2,2]/math.cos(theta))
+        eulers[1,0] = math.atan2(self.rotmat[2,1]/math.cos(theta2), self.rotmat[2,2]/math.cos(theta2))
+
+        # theta1, theta2
+        eulers[0,1] = theta
+        eulers[1,1] = theta2
+
+        # phi1, phi2
+        eulers[0,2] = math.atan2(self.rotmat[1,0]/math.cos(theta), self.rotmat[0,0]/math.cos(theta))
+        eulers[1,2] = math.atan2(self.rotmat[1,0]/math.cos(theta2), self.rotmat[0,0]/math.cos(theta2))
+
+        if use_deg:
+            eulers = rad2deg(eulers)
+
+        return eulers
+    
+    def to_quat(self):
+        #TODO
+        pass
+    
+    def __str__(self):
+        return "Rotation Matrix: \n " + self.rotmat.__str__()
+    
+
+
+

dataloaders/pymo/rotation_tools.py!

@@ -0,0 +1,69 @@
+'''
+Tools for Manipulating and Converting 3D Rotations
+
+By Omid Alemi
+Created: June 12, 2017
+
+Adapted from that matlab file...
+'''
+
+import math
+import numpy as np
+
+def deg2rad(x):
+    return x/180*math.pi
+
+class Rotation():
+    def __init__(self,rot, param_type, **params):
+        self.rotmat = []
+        if param_type == 'euler':
+            self._from_euler(rot[0],rot[1],rot[2], params)
+
+    def _from_euler(self, alpha, beta, gamma, params):
+        '''Expecting degress'''
+
+        if params['from_deg']==True:
+            alpha = deg2rad(alpha)
+            beta = deg2rad(beta)
+            gamma = deg2rad(gamma)
+        
+        Rx = np.asarray([[1, 0, 0], 
+              [0, math.cos(alpha), -math.sin(alpha)], 
+              [0, math.sin(alpha), math.cos(alpha)]
+              ])
+
+        Ry = np.asarray([[math.cos(beta), 0, math.sin(beta)], 
+              [0, 1, 0],
+              [-math.sin(beta), 0, math.cos(beta)]])
+
+        Rz = np.asarray([[math.cos(gamma), -math.sin(gamma), 0],
+              [math.sin(gamma), math.cos(gamma), 0],
+              [0, 0, 1]])
+
+        self.rotmat = np.matmul(np.matmul(Rz, Ry), Rx).T
+    
+    def get_euler_axis(self):
+        R = self.rotmat
+        theta = math.acos((self.rotmat.trace() - 1) / 2)
+        axis = np.asarray([R[2,1] - R[1,2], R[0,2] - R[2,0], R[1,0] - R[0,1]])
+        axis = axis/(2*math.sin(theta))
+        return theta, axis
+
+    def to_expmap(self):
+        theta, axis = self.get_euler_axis()
+        rot_arr = theta * axis
+        if np.isnan(rot_arr).any():
+            rot_arr = [0, 0, 0]
+        return rot_arr
+    
+    def to_euler(self):
+        #TODO
+        pass
+    
+    def to_quat(self):
+        #TODO
+        pass
+    
+
+
+

dataloaders/pymo/viz_tools.py

@@ -0,0 +1,236 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import IPython
+import os
+
+def save_fig(fig_id, tight_layout=True):
+    if tight_layout:
+        plt.tight_layout()
+    plt.savefig(fig_id + '.png', format='png', dpi=300)
+    
+    
+def draw_stickfigure(mocap_track, frame, data=None, joints=None, draw_names=False, ax=None, figsize=(8,8)):
+    if ax is None:
+        fig = plt.figure(figsize=figsize)
+        ax = fig.add_subplot(111)
+    
+    if joints is None:
+        joints_to_draw = mocap_track.skeleton.keys()
+    else:
+        joints_to_draw = joints
+    
+    if data is None:
+        df = mocap_track.values
+    else:
+        df = data
+        
+    for joint in joints_to_draw:
+        ax.scatter(x=df['%s_Xposition'%joint][frame], 
+                   y=df['%s_Yposition'%joint][frame],  
+                   alpha=0.6, c='b', marker='o')
+
+        parent_x = df['%s_Xposition'%joint][frame]
+        parent_y = df['%s_Yposition'%joint][frame]
+        
+        children_to_draw = [c for c in mocap_track.skeleton[joint]['children'] if c in joints_to_draw]
+        
+        for c in children_to_draw:
+            child_x = df['%s_Xposition'%c][frame]
+            child_y = df['%s_Yposition'%c][frame]
+            ax.plot([parent_x, child_x], [parent_y, child_y], 'k-', lw=2)
+            
+        if draw_names:
+            ax.annotate(joint, 
+                    (df['%s_Xposition'%joint][frame] + 0.1, 
+                     df['%s_Yposition'%joint][frame] + 0.1))
+
+    return ax
+
+def draw_stickfigure3d(mocap_track, frame, data=None, joints=None, draw_names=False, ax=None, figsize=(8,8)):
+    from mpl_toolkits.mplot3d import Axes3D
+    
+    if ax is None:
+        fig = plt.figure(figsize=figsize)
+        ax = fig.add_subplot(111, projection='3d') 
+    
+    if joints is None:
+        joints_to_draw = mocap_track.skeleton.keys()
+    else:
+        joints_to_draw = joints
+    
+    if data is None:
+        df = mocap_track.values
+    else:
+        df = data
+        
+    for joint in joints_to_draw:
+        parent_x = df['%s_Xposition'%joint][frame]
+        parent_y = df['%s_Zposition'%joint][frame]
+        parent_z = df['%s_Yposition'%joint][frame]
+        # ^ In mocaps, Y is the up-right axis 
+
+        ax.scatter(xs=parent_x, 
+                   ys=parent_y,  
+                   zs=parent_z,  
+                   alpha=0.6, c='b', marker='o')
+
+        
+        children_to_draw = [c for c in mocap_track.skeleton[joint]['children'] if c in joints_to_draw]
+        
+        for c in children_to_draw:
+            child_x = df['%s_Xposition'%c][frame]
+            child_y = df['%s_Zposition'%c][frame]
+            child_z = df['%s_Yposition'%c][frame]
+            # ^ In mocaps, Y is the up-right axis
+
+            ax.plot([parent_x, child_x], [parent_y, child_y], [parent_z, child_z], 'k-', lw=2, c='black')
+            
+        if draw_names:
+            ax.text(x=parent_x + 0.1, 
+                    y=parent_y + 0.1,
+                    z=parent_z + 0.1,
+                    s=joint,
+                    color='rgba(0,0,0,0.9)')
+
+    return ax
+
+
+def sketch_move(mocap_track, data=None, ax=None, figsize=(16,8)):
+    if ax is None:
+        fig = plt.figure(figsize=figsize)
+        ax = fig.add_subplot(111)
+    
+    if data is None:
+        data = mocap_track.values
+
+    for frame in range(0, data.shape[0], 4):
+#         draw_stickfigure(mocap_track, f, data=data, ax=ax)
+        
+        for joint in mocap_track.skeleton.keys():
+            children_to_draw = [c for c in mocap_track.skeleton[joint]['children']]
+            
+            parent_x = data['%s_Xposition'%joint][frame]
+            parent_y = data['%s_Yposition'%joint][frame]
+            
+            frame_alpha = frame/data.shape[0]
+            
+            for c in children_to_draw:
+                child_x = data['%s_Xposition'%c][frame]
+                child_y = data['%s_Yposition'%c][frame]
+                
+                ax.plot([parent_x, child_x], [parent_y, child_y], '-', lw=1, color='gray', alpha=frame_alpha)
+
+
+
+def viz_cnn_filter(feature_to_viz, mocap_track, data, gap=25):
+    fig = plt.figure(figsize=(16,4))
+    ax = plt.subplot2grid((1,8),(0,0))
+    ax.imshow(feature_to_viz.T, aspect='auto', interpolation='nearest')
+    
+    ax = plt.subplot2grid((1,8),(0,1), colspan=7)
+    for frame in range(feature_to_viz.shape[0]):
+        frame_alpha = 0.2#frame/data.shape[0] * 2 + 0.2
+
+        for joint_i, joint in enumerate(mocap_track.skeleton.keys()):
+            children_to_draw = [c for c in mocap_track.skeleton[joint]['children']]
+
+            parent_x = data['%s_Xposition'%joint][frame] + frame * gap
+            parent_y = data['%s_Yposition'%joint][frame] 
+
+            ax.scatter(x=parent_x, 
+                       y=parent_y,  
+                       alpha=0.6,
+                       cmap='RdBu',
+                       c=feature_to_viz[frame][joint_i] * 10000,
+                       marker='o',
+                       s = abs(feature_to_viz[frame][joint_i] * 10000))
+            plt.axis('off')
+            for c in children_to_draw:
+                child_x = data['%s_Xposition'%c][frame] + frame * gap
+                child_y = data['%s_Yposition'%c][frame] 
+
+                ax.plot([parent_x, child_x], [parent_y, child_y], '-', lw=1, color='gray', alpha=frame_alpha)
+
+                   
+def print_skel(X):
+    stack = [X.root_name]
+    tab=0
+    while stack:
+        joint = stack.pop()
+        tab = len(stack)
+        print('%s- %s (%s)'%('| '*tab, joint, X.skeleton[joint]['parent']))
+        for c in X.skeleton[joint]['children']:
+            stack.append(c)
+
+
+def nb_play_mocap_fromurl(mocap, mf, frame_time=1/30, scale=1, base_url='http://titan:8385'):
+    if mf == 'bvh':
+        bw = BVHWriter()
+        with open('test.bvh', 'w') as ofile:
+            bw.write(mocap, ofile)
+        
+        filepath = '../notebooks/test.bvh'
+    elif mf == 'pos':
+        c = list(mocap.values.columns)
+
+        for cc in c:
+            if 'rotation' in cc:
+                c.remove(cc)
+        mocap.values.to_csv('test.csv', index=False, columns=c)
+        
+        filepath = '../notebooks/test.csv'
+    else:
+        return
+    
+    url = '%s/mocapplayer/player.html?data_url=%s&scale=%f&cz=200&order=xzyi&frame_time=%f'%(base_url, filepath, scale, frame_time)
+    iframe = '<iframe src=' + url + ' width="100%" height=500></iframe>'
+    link = '<a href=%s target="_blank">New Window</a>'%url
+    return IPython.display.HTML(iframe+link)
+
+def nb_play_mocap(mocap, mf, meta=None, frame_time=1/30, scale=1, camera_z=500, base_url=None):
+    data_template = 'var dataBuffer = `$$DATA$$`;'
+    data_template += 'var metadata = $$META$$;'
+    data_template += 'start(dataBuffer, metadata, $$CZ$$, $$SCALE$$, $$FRAMETIME$$);'
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+
+
+    if base_url is None:
+        base_url = os.path.join(dir_path, 'mocapplayer/playBuffer.html')
+    
+    # print(dir_path)
+
+    if mf == 'bvh':
+        pass
+    elif mf == 'pos':
+        cols = list(mocap.values.columns)
+        for c in cols:
+            if 'rotation' in c:
+                cols.remove(c)
+        
+        data_csv = mocap.values.to_csv(index=False, columns=cols)
+
+        if meta is not None:
+            lines = [','.join(item) for item in meta.astype('str')]
+            meta_csv = '[' + ','.join('[%s]'%l for l in lines) +']'            
+        else:
+            meta_csv = '[]'
+        
+        data_assigned = data_template.replace('$$DATA$$', data_csv)
+        data_assigned = data_assigned.replace('$$META$$', meta_csv)
+        data_assigned = data_assigned.replace('$$CZ$$', str(camera_z))
+        data_assigned = data_assigned.replace('$$SCALE$$', str(scale))
+        data_assigned = data_assigned.replace('$$FRAMETIME$$', str(frame_time))
+
+    else:
+        return
+    
+    
+
+    with open(os.path.join(dir_path, 'mocapplayer/data.js'), 'w') as oFile:
+        oFile.write(data_assigned)
+
+    url = '%s?&cz=200&order=xzyi&frame_time=%f&scale=%f'%(base_url, frame_time, scale)
+    iframe = '<iframe frameborder="0" src=' + url + ' width="100%" height=500></iframe>'
+    link = '<a href=%s target="_blank">New Window</a>'%url
+    return IPython.display.HTML(iframe+link)

dataloaders/pymo/writers.py

@@ -0,0 +1,55 @@
+import numpy as np
+import pandas as pd
+
+class BVHWriter():
+    def __init__(self):
+        pass
+    
+    def write(self, X, ofile):
+        
+        # Writing the skeleton info
+        ofile.write('HIERARCHY\n')
+        
+        self.motions_ = []
+        self._printJoint(X, X.root_name, 0, ofile)
+
+        # Writing the motion header
+        ofile.write('MOTION\n')
+        ofile.write('Frames: %d\n'%X.values.shape[0])
+        ofile.write('Frame Time: %f\n'%X.framerate)
+
+        # Writing the data
+        self.motions_ = np.asarray(self.motions_).T
+        lines = [" ".join(item) for item in self.motions_.astype(str)]
+        ofile.write("".join("%s\n"%l for l in lines))
+
+    def _printJoint(self, X, joint, tab, ofile):
+        
+        if X.skeleton[joint]['parent'] == None:
+            ofile.write('ROOT %s\n'%joint)
+        elif len(X.skeleton[joint]['children']) > 0:
+            ofile.write('%sJOINT %s\n'%('\t'*(tab), joint))
+        else:
+            ofile.write('%sEnd site\n'%('\t'*(tab)))
+
+        ofile.write('%s{\n'%('\t'*(tab)))
+        
+        ofile.write('%sOFFSET %3.5f %3.5f %3.5f\n'%('\t'*(tab+1),
+                                                X.skeleton[joint]['offsets'][0],
+                                                X.skeleton[joint]['offsets'][1],
+                                                X.skeleton[joint]['offsets'][2]))
+        channels = X.skeleton[joint]['channels']
+        n_channels = len(channels)
+
+        if n_channels > 0:
+            for ch in channels:
+                self.motions_.append(np.asarray(X.values['%s_%s'%(joint, ch)].values))
+
+        if len(X.skeleton[joint]['children']) > 0:
+            ch_str = ''.join(' %s'*n_channels%tuple(channels))
+            ofile.write('%sCHANNELS %d%s\n' %('\t'*(tab+1), n_channels, ch_str)) 
+
+            for c in X.skeleton[joint]['children']:
+                self._printJoint(X, c, tab+1, ofile)
+
+        ofile.write('%s}\n'%('\t'*(tab)))