def load_mesh(self, data_dict):
gender_type = "male" if data_dict["gender"] == -1 else "female"
with torch.no_grad():
bm = BodyModel(bm_path=self.bm_path%(gender_type), num_betas=self.num_betas, batch_size=1)
body = bm.forward(pose_body=data_dict['pose_body'].unsqueeze(0),
betas=data_dict['betas'].unsqueeze(0))
mesh_ori = trimesh.Trimesh(vertices=c2c(body.v)[0], faces=c2c(body.f))
# move the mesh to the original
joints = c2c(body.Jtr)[0]
root_xyz = joints[0]
mesh_ori.vertices -= root_xyz
verts = mesh_ori.vertices
vert_normals = mesh_ori.vertex_normals
face_normals = mesh_ori.face_normals
faces = mesh_ori.faces
mesh = HoppeMesh(
verts=verts,
faces=faces,
vert_normals=vert_normals,
face_normals=face_normals)
return {'mesh': mesh,
'A': body.A[0,:self.num_poses+1],
'weights': body.weights}
def forward(self, poZ_body=None, **kwargs):
if self.poser_type == 'vposer':
if self.model_type in ['smpl', 'smplh', 'smplx']:
if poZ_body is None: poZ_body = self.poZ_body
pose = self.poser_body_pt.decode(poZ_body, output_type='aa').view(self.batch_size, -1)
if pose.shape[1] > 63:
pose_body = pose[:, 3:66]
root_orient = pose[:, :3]
else:
pose_body = pose[:, :63]
root_orient = None
if self.use_hands and self.model_type in['smplh', 'smplx']:
pose_handL = self.poser_handL_pt.decode(self.poZ_handL, output_type='aa').view(self.batch_size, -1)
pose_handR = self.poser_handR_pt.decode(self.poZ_handR, output_type='aa').view(self.batch_size, -1)
pose_hand = torch.cat([pose_handL, pose_handR], dim=1)
else:
pose_hand = None
new_body = super(BodyModelWithPoser, self).forward(pose_body=pose_body, root_orient=root_orient, pose_hand=pose_hand, **kwargs)
new_body.poZ_body = poZ_body
if self.model_type in ['mano_left', 'mano_right']:
pose_hand = self.poser_hand_pt.decode(self.poZ_hand, output_type='aa').view(self.batch_size, -1)
new_body = super(BodyModelWithPoser, self).forward(pose_hand=pose_hand, **kwargs)
else:
new_body = BodyModel.forward(self)
return new_body
optimizer = torch.optim.Adam([pose_embedding], lr=0.1)
target_locs = torch.Tensor([[0.3, 0, 0.5], [-0.3, 0, 0.5]]).cuda()
wrist_idxs = list(range(20, 22))
for iter_idx in range(100):
pose_body_off = vp.decode(pose_embedding, output_type="aa").view(-1, 63)
if mode == "smplx":
# outputs = model.forward(body_pose=pose_body_off - pose_body)
# outputs = model.forward(body_pose=pose_body_off - pose_body)
outputs = model.forward(body_pose=pose_body_off)
verts = outputs.vertices
joints = outputs.joints
else:
# out = bm.forward(pose_body=pose_body_off - pose_body, return_vertices=True)
out = bm.forward(pose_body=pose_body_off, return_vertices=True)
verts = out.v
joints = out.Jtr
wrist_joints = joints[:, wrist_idxs]
diffs = wrist_joints - target_locs
loss = (diffs ** 2).sum(-1).mean() + 0.001 * (pose_embedding ** 2).sum()
optimizer.zero_grad()
loss.backward(retain_graph=True)
optimizer.step()
# if (pose_embedding != pose_embedding).sum():
# import pdb; pdb.set_trace()
pts = verts.cpu().detach().numpy()
wrists = joints[0, wrist_idxs].cpu().detach().numpy()
targ = target_locs.cpu().detach().numpy()
plt.clf()