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import os
import sys
import numpy as np
import torch
from torch import nn
import pickle
from scipy.interpolate import interp1d
#############Import fast smplx(modified from original ver)
local_smplx_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../..', 'deps/smplx'))
sys.path.insert(0, local_smplx_path)
import smplx_fast
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from utils.transforms import matrix_to_axis_angle, rotation_6d_to_matrix
from utils.constants import pelvis_shift, relaxed_hand_pose, SELECTED_JOINTS24
###########This model is used to predict the initial pose for the optimization###########
class JointsToSMPLX(nn.Module):
def __init__(self, input_dim, output_dim, hidden_dim, **kwargs):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.BatchNorm1d(hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.BatchNorm1d(hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, output_dim),
)
def forward(self, x):
return self.layers(x)
def get_j2s_model(ckpt_path,
input_dim=72,
output_dim=132,
hidden_dim=64,
device='cpu'):
model_joints_to_smplx = JointsToSMPLX(input_dim=input_dim,
output_dim=output_dim,
hidden_dim=hidden_dim
)
if device == 'cpu':
map_location = torch.device('cpu')
else:
map_location = device
model_joints_to_smplx.load_state_dict(torch.load(ckpt_path, map_location=map_location))
model_joints_to_smplx.eval()
return model_joints_to_smplx
###########This model is used to predict the initial pose for the optimization###########
def optimize_smpl(pose_pred, joints, joints_ind, smplx_path, print_loss=True):
device = joints.device
len = joints.shape[0]
smpl_model = smplx_fast.create(smplx_path,
model_type='smplx_joint_only',
gender='male', ext='npz',
num_betas=10,
use_pca=False,
create_global_orient=True,
create_body_pose=True,
create_betas=True,
create_left_hand_pose=True,
create_right_hand_pose=True,
create_expression=True,
create_jaw_pose=True,
create_leye_pose=True,
create_reye_pose=True,
create_transl=True,
batch_size=len,
).to(device)
smpl_model.eval()
joints = joints.reshape(len, -1, 3) + torch.tensor(pelvis_shift).to(device)
pose_input = torch.nn.Parameter(pose_pred.detach(), requires_grad=True)
transl = torch.nn.Parameter(torch.zeros(pose_pred.shape[0], 3).to(device), requires_grad=True)
left_hand = torch.from_numpy(relaxed_hand_pose[:45].reshape(1, -1).repeat(pose_pred.shape[0], axis=0)).to(device)
right_hand = torch.from_numpy(relaxed_hand_pose[45:].reshape(1, -1).repeat(pose_pred.shape[0], axis=0)).to(device)
optimizer = torch.optim.Adam(params=[pose_input, transl], lr=0.05)
loss_fn = nn.MSELoss()
vertices_output = None
for step in range(120):
smpl_output = smpl_model(transl=transl,
body_pose=pose_input[:, 3:],
global_orient=pose_input[:, :3],
return_verts=True,
left_hand_pose=left_hand,# @ left_hand_components[:hand_pca],
right_hand_pose=right_hand,# @ right_hand_components[:hand_pca],
)
joints_output = smpl_output[:, joints_ind].reshape(len, -1, 3)
loss = loss_fn(joints[:, :], joints_output[:, :])
optimizer.zero_grad()
loss.backward()
optimizer.step()
if print_loss:
print(loss.item(), flush=True)
return pose_input.detach().cpu().numpy(), \
transl.detach().cpu().numpy(), \
left_hand.detach().cpu().numpy(), \
right_hand.detach().cpu().numpy(), \
vertices_output
def joints_to_smpl(model, joints, joints_ind, interp_s, smplx_path, print_loss=True):
joints = interpolate_joints(joints, scale=interp_s)
input_len = joints.shape[0]
joints = joints.reshape(input_len, -1, 3)
joints = joints.permute(1, 0, 2)
trans_np = joints[0].detach().cpu().numpy()
joints = joints - joints[0]
joints = joints.permute(1, 0, 2)
joints = joints.reshape(input_len, -1)
pose_pred = model(joints)
pose_pred = pose_pred.reshape(-1, 6)
pose_pred = matrix_to_axis_angle(rotation_6d_to_matrix(pose_pred)).reshape(input_len, -1)
pose_output, transl, left_hand, right_hand, vertices = optimize_smpl(pose_pred,
joints,
joints_ind,
smplx_path,
print_loss=print_loss)
transl = trans_np - np.array(pelvis_shift) + transl
return pose_output, transl, left_hand, right_hand, vertices
def interpolate_joints(joints, scale):
if scale == 1:
return joints
device = joints.device
joints = joints.detach().cpu().numpy()
in_len = joints.shape[0]
out_len = int(in_len * scale)
joints = joints.reshape(in_len, -1)
x = np.array(range(in_len))
xnew = np.linspace(0, in_len - 1, out_len)
f = interp1d(x, joints, axis=0)
joints_new = f(xnew)
joints_new = torch.from_numpy(joints_new).to(device).float()
return joints_new
def process_file(file_path, # input dir
file_name, # input file
save_path, # output dir
JointsToSMPLX_model_path, # JointsToSMPLX weight
smplx_path, # smplx weight
key_list = ['generated_samples', 'original_samples'],
joints_ind = SELECTED_JOINTS24,
interp_s=2, # 2*10=20 fps
):
data = np.load(os.path.join(file_path, file_name), allow_pickle=True)
model = get_j2s_model(ckpt_path=JointsToSMPLX_model_path, device='cpu')
for key in key_list: # original_samples, generated_samples, GT
if key in data:
joints = torch.tensor(data[key], dtype=torch.float32).reshape(-1, 72)
print_loss=False
if key == 'generated_samples':
print_loss=True
pose, transl, left_hand, right_hand, vertices = joints_to_smpl(model,
joints,
joints_ind,
interp_s,
smplx_path,
print_loss=print_loss)
try:
data_text = data['text']
except:
data_text = None
output_data = {
'body_pose': pose[:, 3:],
'global_orient': pose[:, :3],
'transl': transl,
'left_hand': left_hand,
'right_hand': right_hand,
'vertices': vertices,
'text': data_text,
}
if key == 'generated_samples':
try:
output_data['mask'] = data['mask']
except:
output_data['mask'] = None
if not os.path.exists(os.path.join(save_path, key)):
os.makedirs(os.path.join(save_path, key))
output_file = os.path.join(os.path.join(save_path, key), file_name)
with open(output_file, 'wb') as file:
pickle.dump(output_data, file) |