| import torch |
| import torch.nn as nn |
| import numpy as np |
| import os |
|
|
|
|
| class Face_3DMM(nn.Module): |
| def __init__(self, modelpath, id_dim, exp_dim, tex_dim, point_num): |
| super(Face_3DMM, self).__init__() |
| |
| |
| |
| self.point_num = point_num |
| DMM_info = np.load( |
| os.path.join(modelpath, "3DMM_info.npy"), allow_pickle=True |
| ).item() |
| base_id = DMM_info["b_shape"][:id_dim, :] |
| mu_id = DMM_info["mu_shape"] |
| base_exp = DMM_info["b_exp"][:exp_dim, :] |
| mu_exp = DMM_info["mu_exp"] |
| mu = mu_id + mu_exp |
| mu = mu.reshape(-1, 3) |
| for i in range(3): |
| mu[:, i] -= np.mean(mu[:, i]) |
| mu = mu.reshape(-1) |
| self.base_id = torch.as_tensor(base_id).cuda() /1000.0 |
| self.base_exp = torch.as_tensor(base_exp).cuda() /1000.0 |
| self.mu = torch.as_tensor(mu).cuda() /1000.0 |
| base_tex = DMM_info["b_tex"][:tex_dim, :] |
| mu_tex = DMM_info["mu_tex"] |
| self.base_tex = torch.as_tensor(base_tex).cuda() |
| self.mu_tex = torch.as_tensor(mu_tex).cuda() |
| sig_id = DMM_info["sig_shape"][:id_dim] |
| sig_tex = DMM_info["sig_tex"][:tex_dim] |
| sig_exp = DMM_info["sig_exp"][:exp_dim] |
| self.sig_id = torch.as_tensor(sig_id).cuda() |
| self.sig_tex = torch.as_tensor(sig_tex).cuda() |
| self.sig_exp = torch.as_tensor(sig_exp).cuda() |
|
|
| def forward_geo_sub(self, id_para, exp_para, sub_index): |
| id_para = id_para*self.sig_id |
| exp_para = exp_para*self.sig_exp |
| sel_index = torch.cat((3*sub_index.unsqueeze(1), 3*sub_index.unsqueeze(1)+1, |
| 3*sub_index.unsqueeze(1)+2), dim=1).reshape(-1) |
| geometry = torch.mm(id_para, self.base_id[:, sel_index]) + \ |
| torch.mm(exp_para, self.base_exp[:, |
| sel_index]) + self.mu[sel_index] |
| return geometry.reshape(-1, sub_index.shape[0], 3) |
|
|
| def forward_geo(self, id_para, exp_para): |
| id_para = id_para*self.sig_id |
| exp_para = exp_para*self.sig_exp |
| geometry = torch.mm(id_para, self.base_id) + \ |
| torch.mm(exp_para, self.base_exp) + self.mu |
| return geometry.reshape(-1, self.point_num, 3) |
|
|
| def forward_tex(self, tex_para): |
| tex_para = tex_para*self.sig_tex |
| texture = torch.mm(tex_para, self.base_tex) + self.mu_tex |
| return texture.reshape(-1, self.point_num, 3) |
|
|
