Hi, I think the for loop in 'raw_pose_processing.ipynb' may not be appropriate, the SMPL body model can process sequence data which is more efficient.

with torch.no_grad():
    for fId in range(0, frame_number, down_sample):
        root_orient = torch.Tensor(bdata['poses'][fId:fId+1, :3]).to(comp_device) # controls the global root orientation
        pose_body = torch.Tensor(bdata['poses'][fId:fId+1, 3:66]).to(comp_device) # controls the body
        pose_hand = torch.Tensor(bdata['poses'][fId:fId+1, 66:]).to(comp_device) # controls the finger articulation
        betas = torch.Tensor(bdata['betas'][:10][np.newaxis]).to(comp_device) # controls the body shape
        trans = torch.Tensor(bdata['trans'][fId:fId+1]).to(comp_device)    
        body = bm(pose_body=pose_body, pose_hand=pose_hand, betas=betas, root_orient=root_orient)
        joint_loc = body.Jtr[0] + trans
        pose_seq.append(joint_loc.unsqueeze(0))

I change the above code to the following, eliminating the for loop.

down_sample = int(fps / ex_fps)
with torch.no_grad():
    root_orient = torch.Tensor(bdata['poses'][::down_sample, :3]).to(comp_device) # controls the global root orientation
    pose_body = torch.Tensor(bdata['poses'][::down_sample, 3:66]).to(comp_device) # controls the body
    pose_hand = torch.Tensor(bdata['poses'][::down_sample, 66:]).to(comp_device) # controls the finger articulation
    betas = torch.Tensor(bdata['betas'][:10][np.newaxis]).repeat((pose_hand.shape[0], 1)).to(comp_device) # controls the body shape
    trans = torch.Tensor(bdata['trans'][::down_sample]).to(comp_device)    
    body = bm(pose_body=pose_body, pose_hand=pose_hand, betas=betas, root_orient=root_orient)
    joint_loc = body.Jtr[:,:22] + trans[:, None]
    pose_seq.append(joint_loc)

This can reduce the processing time enormously

Time to execute raw_pose_preprocessing #41

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