import torch import os, sys import pickle import smplx import numpy as np sys.path.append(os.path.dirname(__file__)) from customloss import (camera_fitting_loss, body_fitting_loss, camera_fitting_loss_3d, body_fitting_loss_3d, ) from prior import MaxMixturePrior from visualize.joints2smpl.src import config @torch.no_grad() def guess_init_3d(model_joints, j3d, joints_category="orig"): """Initialize the camera translation via triangle similarity, by using the torso joints . :param model_joints: SMPL model with pre joints :param j3d: 25x3 array of Kinect Joints :returns: 3D vector corresponding to the estimated camera translation """ # get the indexed four gt_joints = ['RHip', 'LHip', 'RShoulder', 'LShoulder'] gt_joints_ind = [config.JOINT_MAP[joint] for joint in gt_joints] if joints_category=="orig": joints_ind_category = [config.JOINT_MAP[joint] for joint in gt_joints] elif joints_category=="AMASS": joints_ind_category = [config.AMASS_JOINT_MAP[joint] for joint in gt_joints] else: print("NO SUCH JOINTS CATEGORY!") sum_init_t = (j3d[:, joints_ind_category] - model_joints[:, gt_joints_ind]).sum(dim=1) init_t = sum_init_t / 4.0 return init_t # SMPLIfy 3D class SMPLify3D(): """Implementation of SMPLify, use 3D joints.""" def __init__(self, smplxmodel, step_size=1e-2, batch_size=1, num_iters=100, use_collision=False, use_lbfgs=True, joints_category="orig", device=torch.device('cuda:0'), ): # Store options self.batch_size = batch_size self.device = device self.step_size = step_size self.num_iters = num_iters # --- choose optimizer self.use_lbfgs = use_lbfgs # GMM pose prior self.pose_prior = MaxMixturePrior(prior_folder=config.GMM_MODEL_DIR, num_gaussians=8, dtype=torch.float32).to(device) # collision part self.use_collision = use_collision if self.use_collision: self.part_segm_fn = config.Part_Seg_DIR # reLoad SMPL-X model self.smpl = smplxmodel self.model_faces = smplxmodel.faces_tensor.view(-1) # select joint joint_category self.joints_category = joints_category if joints_category=="orig": self.smpl_index = config.full_smpl_idx self.corr_index = config.full_smpl_idx elif joints_category=="AMASS": self.smpl_index = config.amass_smpl_idx self.corr_index = config.amass_idx else: self.smpl_index = None self.corr_index = None print("NO SUCH JOINTS CATEGORY!") # ---- get the man function here ------ def __call__(self, init_pose, init_betas, init_cam_t, j3d, conf_3d=1.0, seq_ind=0): """Perform body fitting. Input: init_pose: SMPL pose estimate init_betas: SMPL betas estimate init_cam_t: Camera translation estimate j3d: joints 3d aka keypoints conf_3d: confidence for 3d joints seq_ind: index of the sequence Returns: vertices: Vertices of optimized shape joints: 3D joints of optimized shape pose: SMPL pose parameters of optimized shape betas: SMPL beta parameters of optimized shape camera_translation: Camera translation """ # # # add the mesh inter-section to avoid search_tree = None pen_distance = None filter_faces = None if self.use_collision: from mesh_intersection.bvh_search_tree import BVH import mesh_intersection.loss as collisions_loss from mesh_intersection.filter_faces import FilterFaces search_tree = BVH(max_collisions=8) pen_distance = collisions_loss.DistanceFieldPenetrationLoss( sigma=0.5, point2plane=False, vectorized=True, penalize_outside=True) if self.part_segm_fn: # Read the part segmentation part_segm_fn = os.path.expandvars(self.part_segm_fn) with open(part_segm_fn, 'rb') as faces_parents_file: face_segm_data = pickle.load(faces_parents_file, encoding='latin1') faces_segm = face_segm_data['segm'] faces_parents = face_segm_data['parents'] # Create the module used to filter invalid collision pairs filter_faces = FilterFaces( faces_segm=faces_segm, faces_parents=faces_parents, ign_part_pairs=None).to(device=self.device) # Split SMPL pose to body pose and global orientation body_pose = init_pose[:, 3:].detach().clone() global_orient = init_pose[:, :3].detach().clone() betas = init_betas.detach().clone() # use guess 3d to get the initial smpl_output = self.smpl(global_orient=global_orient, body_pose=body_pose, betas=betas) model_joints = smpl_output.joints init_cam_t = guess_init_3d(model_joints, j3d, self.joints_category).unsqueeze(1).detach() camera_translation = init_cam_t.clone() preserve_pose = init_pose[:, 3:].detach().clone() # -------------Step 1: Optimize camera translation and body orientation-------- # Optimize only camera translation and body orientation body_pose.requires_grad = False betas.requires_grad = False global_orient.requires_grad = True camera_translation.requires_grad = True camera_opt_params = [global_orient, camera_translation] if self.use_lbfgs: camera_optimizer = torch.optim.LBFGS(camera_opt_params, max_iter=self.num_iters, lr=self.step_size, line_search_fn='strong_wolfe') for i in range(10): def closure(): camera_optimizer.zero_grad() smpl_output = self.smpl(global_orient=global_orient, body_pose=body_pose, betas=betas) model_joints = smpl_output.joints # print('model_joints', model_joints.shape) # print('camera_translation', camera_translation.shape) # print('init_cam_t', init_cam_t.shape) # print('j3d', j3d.shape) loss = camera_fitting_loss_3d(model_joints, camera_translation, init_cam_t, j3d, self.joints_category) loss.backward() return loss camera_optimizer.step(closure) else: camera_optimizer = torch.optim.Adam(camera_opt_params, lr=self.step_size, betas=(0.9, 0.999)) for i in range(20): smpl_output = self.smpl(global_orient=global_orient, body_pose=body_pose, betas=betas) model_joints = smpl_output.joints loss = camera_fitting_loss_3d(model_joints[:, self.smpl_index], camera_translation, init_cam_t, j3d[:, self.corr_index], self.joints_category) camera_optimizer.zero_grad() loss.backward() camera_optimizer.step() # Fix camera translation after optimizing camera # --------Step 2: Optimize body joints -------------------------- # Optimize only the body pose and global orientation of the body body_pose.requires_grad = True global_orient.requires_grad = True camera_translation.requires_grad = True # --- if we use the sequence, fix the shape if seq_ind == 0: betas.requires_grad = True body_opt_params = [body_pose, betas, global_orient, camera_translation] else: betas.requires_grad = False body_opt_params = [body_pose, global_orient, camera_translation] if self.use_lbfgs: body_optimizer = torch.optim.LBFGS(body_opt_params, max_iter=self.num_iters, lr=self.step_size, line_search_fn='strong_wolfe') for i in range(self.num_iters): def closure(): body_optimizer.zero_grad() smpl_output = self.smpl(global_orient=global_orient, body_pose=body_pose, betas=betas) model_joints = smpl_output.joints model_vertices = smpl_output.vertices loss = body_fitting_loss_3d(body_pose, preserve_pose, betas, model_joints[:, self.smpl_index], camera_translation, j3d[:, self.corr_index], self.pose_prior, joints3d_conf=conf_3d, joint_loss_weight=600.0, pose_preserve_weight=5.0, use_collision=self.use_collision, model_vertices=model_vertices, model_faces=self.model_faces, search_tree=search_tree, pen_distance=pen_distance, filter_faces=filter_faces) loss.backward() return loss body_optimizer.step(closure) else: body_optimizer = torch.optim.Adam(body_opt_params, lr=self.step_size, betas=(0.9, 0.999)) for i in range(self.num_iters): smpl_output = self.smpl(global_orient=global_orient, body_pose=body_pose, betas=betas) model_joints = smpl_output.joints model_vertices = smpl_output.vertices loss = body_fitting_loss_3d(body_pose, preserve_pose, betas, model_joints[:, self.smpl_index], camera_translation, j3d[:, self.corr_index], self.pose_prior, joints3d_conf=conf_3d, joint_loss_weight=600.0, use_collision=self.use_collision, model_vertices=model_vertices, model_faces=self.model_faces, search_tree=search_tree, pen_distance=pen_distance, filter_faces=filter_faces) body_optimizer.zero_grad() loss.backward() body_optimizer.step() # Get final loss value with torch.no_grad(): smpl_output = self.smpl(global_orient=global_orient, body_pose=body_pose, betas=betas, return_full_pose=True) model_joints = smpl_output.joints model_vertices = smpl_output.vertices final_loss = body_fitting_loss_3d(body_pose, preserve_pose, betas, model_joints[:, self.smpl_index], camera_translation, j3d[:, self.corr_index], self.pose_prior, joints3d_conf=conf_3d, joint_loss_weight=600.0, use_collision=self.use_collision, model_vertices=model_vertices, model_faces=self.model_faces, search_tree=search_tree, pen_distance=pen_distance, filter_faces=filter_faces) vertices = smpl_output.vertices.detach() joints = smpl_output.joints.detach() pose = torch.cat([global_orient, body_pose], dim=-1).detach() betas = betas.detach() return vertices, joints, pose, betas, camera_translation, final_loss