| import torch |
| import torch.nn as nn |
| import torch.nn.parallel |
| import torch.utils.data |
| from torch.autograd import Variable |
| import numpy as np |
| import torch.nn.functional as F |
|
|
| from manopth.manolayer import ManoLayer |
|
|
|
|
|
|
| def create_mano_layers(mano_path, device, n_cmps): |
| class Output: |
| def __init__(self, vertices, joints): |
| self.vertices = vertices |
| self.joints = joints |
|
|
| class SmplxAdapter: |
| def __init__(self, side): |
| self.m = ManoLayer(mano_root=f'{mano_path}/mano', use_pca=True, ncomps=n_cmps, side=side, flat_hand_mean=False, robust_rot=True).to(device) |
| self.faces = self.m.th_faces.cpu().numpy() |
| self.shapedirs = self.m.th_shapedirs |
|
|
| def __call__(self, global_orient, hand_pose, betas, transl): |
| vertices, joints = self.m(torch.cat([global_orient, hand_pose], 1), betas, transl) |
| |
| vertices /= 1000 |
| joints /= 1000 |
|
|
| return Output(vertices, joints) |
| |
| mano_layer = { |
| 'left': SmplxAdapter(side='left'), |
| 'right': SmplxAdapter(side='right') |
| } |
| |
| if torch.sum(torch.abs(mano_layer['left'].m.th_shapedirs[:,0,:] - mano_layer['right'].m.th_shapedirs[:,0,:])) < 1: |
| print('Fix th_shapedirs bug of MANO') |
| mano_layer['left'].m.th_shapedirs[:,0,:] *= -1 |
|
|
| return mano_layer |
|
|
