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luni
2022-09-02 14:15:54 +00:00
parent ee8f1cb68c
commit 977e49900d
16 changed files with 2241 additions and 272 deletions
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custom_utils/smpl.py
+6 -5
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@@ -69,6 +69,7 @@ class SMPL:
self.nn = np.array(sio.loadmat('custom_utils/NN.mat'))
self.to_keep = sio.loadmat(os.path.join('custom_utils/keep_remove.mat'))['keep'].reshape(-1)
self.weights_all = self.weights
self.weights = tf.gather(self.weights, self.to_keep, axis=1)
def Q2R(self, w, x, y, z):
@@ -132,7 +133,7 @@ class SMPL:
v_shaped.append(tf.tensordot(betas[:,:10], self.shapedirs[1], axes=[[1], [2]]) + tf.reshape(self.v_template[1], shape=(1,27578,3))) # male
v_shaped = tf.stack(v_shaped, axis=-1)
def single_run(x, J_regressor=self.J_regressor, posedirs=self.posedirs, weights=self.weights, parent=self.parent):
def single_run(x, J_regressor=self.J_regressor, posedirs=self.posedirs, weights=self.weights_all, parent=self.parent):
R = x[0]
v_shaped = x[1]
g = x[2]
@@ -160,7 +161,7 @@ class SMPL:
return v, tf.squeeze(J), v_posed
gender = tf.cast(tf.greater_equal(betas[:, 10] , 0), tf.int32)
gender = tf.cast(tf.greater_equal(betas[:, 10] , 0.5), tf.int32)
T_out, J_out, v_posed = tf.map_fn(single_run, (R, v_shaped, gender), (tf.float32, tf.float32, tf.float32))
J0 = tf.reshape(J_out[:,0,:], (-1, 1, 3))
@@ -215,7 +216,7 @@ class SMPL:
return v, tf.squeeze(J)
gender = tf.cast(tf.greater_equal(betas[:, 10] , 0), tf.int32)
gender = tf.cast(tf.greater_equal(betas[:, 10] , 0.5), tf.int32)
T_out, J_out = tf.map_fn(single_run, (R, v_shaped, gender, v_cloth), (tf.float32, tf.float32))
J0 = tf.reshape(J_out[:,0,:], (-1, 1, 3))
@@ -235,7 +236,7 @@ class SMPL:
J = tf.matmul(J_regressor[g], v_shaped[:, :, g])
return J
gender = tf.cast(tf.greater(betas[:, 10] , 0), tf.int32)
gender = tf.cast(tf.greater(betas[:, 10] , 0.5), tf.int32)
J = tf.map_fn(single_run, (v_shaped, gender), (tf.float32))
J0 = tf.reshape(J[:,0,:], (-1, 1, 3))
@@ -274,7 +275,7 @@ class SMPL:
return v, tf.squeeze(J)
gender = tf.cast(tf.greater(betas[:, 10] , 0), tf.int32)
gender = tf.cast(tf.greater(betas[:, 10] , 0.5), tf.int32)
T_out, J_out = tf.map_fn(single_run, (R, J, gender, v_cloth), (tf.float32, tf.float32))
J0 = tf.reshape(J_out[:,0,:], (-1, 1, 3))