additional smpl files

superSMPL/smpl_torch.py

@@ -50,6 +50,10 @@ class SMPL(Module):
     self.f_skirt = np.array(F['faces_skirt'])
     self.nn = np.array(sio.loadmat(os.path.join(path, 'NN.mat')))
     self.to_keep = sio.loadmat(os.path.join(path, 'keep_remove.mat'))['keep'].reshape(-1)
+    self.f_all = np.asarray(female['f'])
+    # convert quad faces to triangles
+    self.f_all = np.concatenate([self.f_all[:,:3], self.f_all[:,[0,2,3]]], axis=0)
+    
 
     self.weights_all = self.weights
     self.weights = self.weights[:,self.to_keep,:]
@@ -205,8 +209,9 @@ class SMPL(Module):
       )
     
     joints = torch.matmul(results, torch.transpose(torch.concat([J, torch.ones((J.shape[0], 24, 1), dtype=torch.float32, device=J.get_device())], axis=-1)[:,:,None,:],2,3))[:,:,:3,0]
-            
+    
-    T = torch.stack([torch.tensordot(results[i], self.weights[g.to(torch.long)], dims=([0], [1])).permute(2, 0, 1) for i, g in enumerate(betas[:,-1])])
+    weights = self.weights if remove else self.weights_all
+    T = torch.stack([torch.tensordot(results[i], weights[g.to(torch.long)], dims=([0], [1])).permute(2, 0, 1) for i, g in enumerate(betas[:,-1])])
     rest_shape_h = torch.cat(
       (v_posed, torch.ones((batch_num, v_posed.shape[1], 1), dtype=torch.float32).to(self.device)), dim=2
     )