backup

custom_utils/smpl.py

@@ -34,12 +34,14 @@ eval_joints = {
         
 class SMPL:
 
-    def __init__(self, orig_smpl=False, use_one_hot=False):
+    def __init__(self, orig_smpl=False, use_one_hot=False, is_skirt=False):
         self.J_regressor = []
         self.v_template = []
         self.shapedirs = []
         self.posedirs = []
         self.weights = []
+        self.use_one_hot = use_one_hot
+        self.is_skirt = is_skirt
         # Load SMPL models
         # path = os.path.abspath(os.path.dirname(__file__))
         with open(os.path.join('custom_utils/super_model_f.pkl'), 'rb') as f:
@@ -299,7 +301,22 @@ class SMPL:
 
     def run_garment(self, pose, betas, v_cloth, v_shaped=None):
         J = self.get_joints_template(betas, v_shaped)            
-        R = self.run_rodrigues(pose)
+
+        if self.use_one_hot and self.is_skirt:
+            # p_avg = (pose[:, 3:6] + pose[:, 6:9]) / 2
+            # pose_dress = tf.concat([pose[:, :3], p_avg, p_avg, pose[:, 9:]], axis=-1)
+            # R = self.run_rodrigues(pose_dress).numpy()
+            # for b in range(betas.shape[0]):
+            #     R[b][[4,5,7,8,10,11],:,:] = np.eye(3)
+            
+            pose_dress = pose
+            # p_avg = (pose_dress[:, 3:6] + pose_dress[:, 6:9]) / 2
+            # pose_dress[:,3:9] = np.concatenate([p_avg, p_avg], axis=-1)
+            R = self.run_rodrigues(pose_dress).numpy()
+            for b in range(betas.shape[0]):
+                R[b][[4,5,7,8,10,11],:,:] = np.eye(3)
+        else:
+            R = self.run_rodrigues(pose)
         
         # if filter is not None:
         #     weights = tf.boolean_mask(self.weights, filter)