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smpl_tf.py
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smpl_tf.py
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import tensorflow as tf
import numpy as np
import pickle
def rodrigues(r):
"""
Rodrigues' rotation formula that turns axis-angle tensor into rotation
matrix in a batch-ed manner.
Parameter:
----------
r: Axis-angle rotation tensor of shape [batch_size, 1, 3].
Return:
-------
Rotation matrix of shape [batch_size, 3, 3].
"""
theta = tf.norm(r + tf.random_normal(r.shape, 0, 1e-8, dtype=tf.float64), axis=(1, 2), keepdims=True)
# avoid divide by zero
r_hat = r / theta
cos = tf.cos(theta)
z_stick = tf.zeros(theta.get_shape().as_list()[0], dtype=tf.float64)
m = tf.stack(
(z_stick, -r_hat[:, 0, 2], r_hat[:, 0, 1], r_hat[:, 0, 2], z_stick,
-r_hat[:, 0, 0], -r_hat[:, 0, 1], r_hat[:, 0, 0], z_stick), axis=1)
m = tf.reshape(m, (-1, 3, 3))
i_cube = tf.expand_dims(tf.eye(3, dtype=tf.float64), axis=0) + tf.zeros(
(theta.get_shape().as_list()[0], 3, 3), dtype=tf.float64)
A = tf.transpose(r_hat, (0, 2, 1))
B = r_hat
dot = tf.matmul(A, B)
R = cos * i_cube + (1 - cos) * dot + tf.sin(theta) * m
return R
def with_zeros(x):
"""
Append a [0, 0, 0, 1] tensor to a [3, 4] tensor.
Parameter:
---------
x: Tensor to be appended.
Return:
------
Tensor after appending of shape [4,4]
"""
ret = tf.concat(
(x, tf.constant([[0.0, 0.0, 0.0, 1.0]], dtype=tf.float64)),
axis=0
)
return ret
def pack(x):
"""
Append zero tensors of shape [4, 3] to a batch of [4, 1] shape tensor.
Parameter:
----------
x: A tensor of shape [batch_size, 4, 1]
Return:
------
A tensor of shape [batch_size, 4, 4] after appending.
"""
ret = tf.concat(
(tf.zeros((x.get_shape().as_list()[0], 4, 3), dtype=tf.float64), x),
axis=2
)
return ret
def smpl_model(model_path, betas, pose, trans, simplify=False):
"""
Construct a compute graph that takes in parameters and outputs a tensor as
model vertices. Face indices are also returned as a numpy ndarray.
Parameters:
---------
pose: Also known as 'theta', a [24,3] tensor indicating child joint rotation
relative to parent joint. For root joint it's global orientation.
Represented in a axis-angle format.
betas: Parameter for model shape. A tensor of shape [10] as coefficients of
PCA components. Only 10 components were released by SMPL author.
trans: Global translation tensor of shape [3].
Return:
------
A tensor for vertices, and a numpy ndarray as face indices.
"""
# For detailed comments see smpl_np.py
with open(model_path, 'rb') as f:
params = pickle.load(f)
J_regressor = tf.constant(
np.array(params['J_regressor'].todense(),
dtype=np.float64)
)
weights = tf.constant(params['weights'], dtype=np.float64)
posedirs = tf.constant(params['posedirs'], dtype=np.float64)
v_template = tf.constant(params['v_template'], dtype=np.float64)
shapedirs = tf.constant(params['shapedirs'], dtype=np.float64)
f = params['f']
kintree_table = params['kintree_table']
id_to_col = {kintree_table[1, i]: i for i in range(kintree_table.shape[1])}
parent = {
i: id_to_col[kintree_table[0, i]]
for i in range(1, kintree_table.shape[1])
}
v_shaped = tf.tensordot(shapedirs, betas, axes=[[2], [0]]) + v_template
J = tf.matmul(J_regressor, v_shaped)
pose_cube = tf.reshape(pose, (-1, 1, 3))
R_cube_big = rodrigues(pose_cube)
if simplify:
v_posed = v_shaped
else:
R_cube = R_cube_big[1:]
I_cube = tf.expand_dims(tf.eye(3, dtype=tf.float64), axis=0) + \
tf.zeros((R_cube.get_shape()[0], 3, 3), dtype=tf.float64)
lrotmin = tf.squeeze(tf.reshape((R_cube - I_cube), (-1, 1)))
v_posed = v_shaped + tf.tensordot(posedirs, lrotmin, axes=[[2], [0]])
results = []
results.append(
with_zeros(tf.concat((R_cube_big[0], tf.reshape(J[0, :], (3, 1))), axis=1))
)
for i in range(1, kintree_table.shape[1]):
results.append(
tf.matmul(
results[parent[i]],
with_zeros(
tf.concat(
(R_cube_big[i], tf.reshape(J[i, :] - J[parent[i], :], (3, 1))),
axis=1
)
)
)
)
stacked = tf.stack(results, axis=0)
results = stacked - \
pack(
tf.matmul(
stacked,
tf.reshape(
tf.concat((J, tf.zeros((24, 1), dtype=tf.float64)), axis=1),
(24, 4, 1)
)
)
)
T = tf.tensordot(weights, results, axes=((1), (0)))
rest_shape_h = tf.concat(
(v_posed, tf.ones((v_posed.get_shape().as_list()[0], 1), dtype=tf.float64)),
axis=1
)
v = tf.matmul(T, tf.reshape(rest_shape_h, (-1, 4, 1)))
v = tf.reshape(v, (-1, 4))[:, :3]
result = v + tf.reshape(trans, (1, 3))
return result, f
if __name__ == '__main__':
pose_size = 72
beta_size = 10
np.random.seed(9608)
pose = (np.random.rand(pose_size) - 0.5) * 0.4
betas = (np.random.rand(beta_size) - 0.5) * 0.06
trans = np.zeros(3)
pose = tf.constant(pose, dtype=tf.float64)
betas = tf.constant(betas, dtype=tf.float64)
trans = tf.constant(trans, dtype=tf.float64)
output, faces = smpl_model('./model.pkl', betas, pose, trans, True)
sess = tf.Session()
result = sess.run(output)
outmesh_path = './smpl_tf.obj'
with open(outmesh_path, 'w') as fp:
for v in result:
fp.write('v %f %f %f\n' % (v[0], v[1], v[2]))
for f in faces + 1:
fp.write('f %d %d %d\n' % (f[0], f[1], f[2]))