346 lines
17 KiB
Python
346 lines
17 KiB
Python
import os
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import sys
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import pickle
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import scipy.io as sio
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from math import *
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import numpy as np
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import tensorflow as tf
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# Left-Right
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eval_joints = {
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'All': [1, 2, 3, 4, 5, 7, 8, 9, 12, 15, 16, 17, 18, 19, 20, 21],
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'Head': [12, 15], # Neck, Head
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'Torso': [3, 6, 9], # Back joints
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'Shoulder': [13, 14, 16, 17], # Inner, Outer
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'Elbow': [18, 19],
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'Wrist': [20, 21],
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'Hip': [1, 2],
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'Knee': [4, 5],
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'Foot': [7, 8, 10, 11]} # Ankle, Foot
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class SMPL:
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def __init__(self, path):
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self.J_regressor = []
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self.v_template = []
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self.shapedirs = []
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self.posedirs = []
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self.weights = []
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# Load SMPL models
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# path = os.path.abspath(os.path.dirname(__file__))
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with open(os.path.join(path, 'super_model_f.pkl'), 'rb') as f:
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if sys.version_info[0]<3:
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female = pickle.load(f)
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else:
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female = pickle.load(f, encoding="latin1")
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self.J_regressor.append(tf.constant(np.array(female['J_regressor'].todense(), dtype=np.float32)))
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self.v_template.append(tf.constant(female['v_template'], dtype=np.float32))
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self.posedirs.append(tf.constant(female['posedirs'], dtype=np.float32))
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self.shapedirs.append(tf.constant(female['shapedirs'], dtype=np.float32))
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self.weights.append(tf.constant(female['weights'], dtype=np.float32))
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with open(os.path.join(path, 'super_model_m.pkl'), 'rb') as f:
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if sys.version_info[0]<3:
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male = pickle.load(f)
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else:
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male = pickle.load(f, encoding="latin1")
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self.J_regressor.append(tf.constant(np.array(male['J_regressor'].todense(), dtype=np.float32)))
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self.v_template.append(tf.constant(male['v_template'], dtype=np.float32))
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self.posedirs.append(tf.constant(male['posedirs'], dtype=np.float32))
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self.shapedirs.append(tf.constant(male['shapedirs'], dtype=np.float32))
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self.weights.append(tf.constant(male['weights'], dtype=np.float32))
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kintree_table = male['kintree_table']
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id_to_col = {kintree_table[1, i]: i for i in range(kintree_table.shape[1])}
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self.parent = {i: id_to_col[kintree_table[0, i]] for i in range(1, kintree_table.shape[1])}
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self.J_regressor = tf.stack(self.J_regressor, axis=0)
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self.v_template = tf.stack(self.v_template, axis=0)
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self.shapedirs = tf.stack(self.shapedirs, axis=0)
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self.posedirs = tf.stack(self.posedirs, axis=0)
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self.weights = tf.stack(self.weights, axis=0)
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self.weights_one_hot = tf.reduce_mean(self.weights, axis=0)
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self.weights_one_hot = tf.math.argmax(self.weights_one_hot, axis=-1)
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self.weights_one_hot = tf.one_hot(self.weights_one_hot, 24)
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# Topology no head/hands/feet
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F = sio.loadmat(path + '/ssmpl_faces.mat')
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self.f = np.array(F['faces_default'])
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self.f_skirt = np.array(F['faces_skirt'])
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self.nn = np.array(sio.loadmat(path + '/NN.mat'))
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self.to_keep = sio.loadmat(os.path.join(path, 'keep_remove.mat'))['keep'].reshape(-1)
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self.weights = tf.gather(self.weights, self.to_keep, axis=1)
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# self.shapedirs = tf.gather(self.shapedirs, self.to_keep, axis=1)
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# self.posedirs = tf.gather(self.posedirs, self.to_keep, axis=1)
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# self.J_regressor = tf.gather(self.J_regressor, self.to_keep, axis=2)
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# self.v_template = tf.gather(self.v_template, self.to_keep, axis=1)
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def Q2R(self, w, x, y, z):
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def diag(a, b): # computes the diagonal entries, 1 - 2*a**2 - 2*b**2
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return 1 - 2 * tf.pow(a, 2) - 2 * tf.pow(b, 2)
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def tr_add(a, b, c, d): # computes triangle entries with addition
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return 2 * a * b + 2 * c * d
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def tr_sub(a, b, c, d): # computes triangle entries with subtraction
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return 2 * a * b - 2 * c * d
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m = [[diag(y, z), tr_sub(x, y, z, w), tr_add(x, z, y, w)],
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[tr_add(x, y, z, w), diag(x, z), tr_sub(y, z, x, w)],
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[tr_sub(x, z, y, w), tr_add(y, z, x, w), diag(x, y)]]
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R = tf.stack([tf.stack(m[i], axis=-1) for i in range(3)], axis=-2)
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return R
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def rodrigues(self, r):
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theta = tf.norm(r, axis=(1, 2), keepdims=True)
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theta = tf.maximum(theta, np.finfo(np.float32).eps)
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r_hat = r / theta
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z_stick = tf.zeros(theta.get_shape().as_list()[0], dtype=tf.float32)
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m = tf.stack(
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(z_stick, -r_hat[:, 0, 2], r_hat[:, 0, 1], r_hat[:, 0, 2], z_stick,
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-r_hat[:, 0, 0], -r_hat[:, 0, 1], r_hat[:, 0, 0], z_stick), axis=1)
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m = tf.reshape(m, (-1, 3, 3))
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i_cube = tf.expand_dims(tf.eye(3, dtype=tf.float32), axis=0) + tf.zeros(
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(theta.get_shape().as_list()[0], 3, 3), dtype=tf.float32)
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# R = cos * i_cube + (1 - cos) * dot + tf.sin(theta) * m
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R = i_cube + m * tf.sin(theta) + tf.matmul(m, m) * (1 - tf.cos(theta))
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return R
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def with_zeros(self, x):
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return tf.concat((x, tf.ones((x.shape[0], 1, 4)) * tf.constant([[0.0, 0.0, 0.0, 1.0]], dtype=tf.float32)[tf.newaxis,:,:]), axis=1)
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def pack(self, x):
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return tf.concat((tf.zeros((x.shape[0], 24, 4, 3), dtype=tf.float32), x), axis=3)
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def with_zeros_old(self, x):
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return tf.concat((x, tf.constant([[0.0, 0.0, 0.0, 1.0]], dtype=tf.float32)), axis=0)
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def pack_old(self, x):
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return tf.concat((tf.zeros((x.get_shape().as_list()[0], 4, 3), dtype=tf.float32), x), axis=2)
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def run_rodrigues(self, pose):
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batch_size = pose.shape[0]
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R_out = self.rodrigues(tf.reshape(pose, (-1, 1, 3)))
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R_out = tf.reshape(R_out, (batch_size, -1, 3, 3))
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#R_out = []
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#for i in range(batch_size):
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# R_out.append(self.rodrigues(tf.reshape(pose[i,:], (-1, 1, 3))))
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#R_out = tf.stack(R_out, axis=0)
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return R_out
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def run(self, R, betas, remove=True):
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batch_size = betas.shape[0]
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#J_regressor = tf.gather(self.J_regressor, gender, axis=0)
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#shapedirs = tf.gather(self.shapedirs, gender, axis=0)
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#v_template = tf.gather(self.v_template, gender, axis=0)
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v_shaped = []
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v_shaped.append(tf.tensordot(betas[:,:10], self.shapedirs[0], axes=[[1], [2]]) + tf.reshape(self.v_template[0], shape=(1,27578,3))) # female
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v_shaped.append(tf.tensordot(betas[:,:10], self.shapedirs[1], axes=[[1], [2]]) + tf.reshape(self.v_template[1], shape=(1,27578,3))) # male
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v_shaped = tf.stack(v_shaped, axis=-1)
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def single_run(x, J_regressor=self.J_regressor, posedirs=self.posedirs, weights=self.weights, parent=self.parent):
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R = x[0]
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v_shaped = x[1]
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g = x[2]
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J = tf.matmul(J_regressor[g], v_shaped[:, :, g])
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R_cube_big = R
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R_cube = R_cube_big[1:]
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I_cube = tf.expand_dims(tf.eye(3, dtype=tf.float32), axis=0) + tf.zeros((R_cube.get_shape()[0], 3, 3), dtype=tf.float32)
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lrotmin = tf.squeeze(tf.reshape((R_cube - I_cube), (-1, 1)))
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v_posed = v_shaped[:, :, g] + tf.tensordot(posedirs[g], lrotmin, axes=[[2], [0]])
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results = []
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results.append(self.with_zeros(tf.concat((R_cube_big[0], tf.reshape(J[0, :], (3, 1))), axis=1)))
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for j in range(1, 24):
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results.append(tf.matmul(results[parent[j]], self.with_zeros(tf.concat((R_cube_big[j], tf.reshape(J[j, :] - J[parent[j], :], (3, 1))), axis=1))))
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stacked = tf.stack(results, axis=0)
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results = stacked - self.pack(tf.matmul(stacked, tf.reshape(tf.concat((J, tf.zeros((24, 1), dtype=tf.float32)), axis=1), (24, 4, 1))))
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J = tf.matmul(results, tf.expand_dims(tf.concat([J, tf.ones([24, 1], tf.float32)], axis=1), axis=1), transpose_b=True)
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J = tf.gather(J, tf.constant([0, 1, 2], tf.int32), axis=1)
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T = tf.tensordot(weights[g], results, axes=((1), (0)))
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rest_shape_h = tf.concat((v_posed, tf.ones((v_posed.get_shape().as_list()[0], 1), dtype=tf.float32)), axis=1)
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v = tf.matmul(T, tf.reshape(rest_shape_h, (-1, 4, 1)))
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v = tf.reshape(v, (-1, 4))[:, :3]
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return v, tf.squeeze(J), v_posed
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gender = tf.cast(tf.greater_equal(betas[:, 10] , 0), tf.int32)
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T_out, J_out, v_posed = tf.map_fn(single_run, (R, v_shaped, gender), (tf.float32, tf.float32, tf.float32))
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J0 = tf.reshape(J_out[:,0,:], (-1, 1, 3))
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J_out = tf.subtract(J_out, J0)
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T_out = tf.subtract(T_out, J0)
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v_posed = tf.subtract(v_posed, J0)
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if remove:
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T_out = tf.gather(T_out, self.to_keep, axis=1)
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v_posed = tf.gather(v_posed, self.to_keep, axis=1)
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return T_out, J_out, v_posed
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def run_with_weights(self, R, betas, v_cloth, weights=None, filter=None):
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v_shaped = []
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v_shaped.append(tf.tensordot(betas[:,:10], self.shapedirs[0], axes=[[1], [2]]) + tf.reshape(self.v_template[0], shape=(1,27578,3))) # female
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v_shaped.append(tf.tensordot(betas[:,:10], self.shapedirs[1], axes=[[1], [2]]) + tf.reshape(self.v_template[1], shape=(1,27578,3))) # male
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v_shaped = tf.stack(v_shaped, axis=-1)
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if weights is None:
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w = self.weights_one_hot[np.newaxis, :, :]
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if filter is not None:
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w = tf.gather(tf.gather(w, self.to_keep, axis=1), filter, axis=1)
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if v_cloth.shape[1] == 14475:
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w = tf.gather(w, self.to_keep, axis=1)
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else:
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w = weights
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def single_run(x, J_regressor=self.J_regressor, w=w, parent=self.parent):
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R = x[0]
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v_shaped = x[1]
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g = x[2]
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v_cloth = x[3]
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J = tf.matmul(J_regressor[g], v_shaped[:, :, g])
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R_cube_big = R
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results = []
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results.append(self.with_zeros(tf.concat((R_cube_big[0], tf.reshape(J[0, :], (3, 1))), axis=1)))
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for j in range(1, 24):
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results.append(tf.matmul(results[parent[j]], self.with_zeros(tf.concat((R_cube_big[j], tf.reshape(J[j, :] - J[parent[j], :], (3, 1))), axis=1))))
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stacked = tf.stack(results, axis=0)
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results = stacked - self.pack(tf.matmul(stacked, tf.reshape(tf.concat((J, tf.zeros((24, 1), dtype=tf.float32)), axis=1), (24, 4, 1))))
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J = tf.matmul(results, tf.expand_dims(tf.concat([J, tf.ones([24, 1], tf.float32)], axis=1), axis=1), transpose_b=True)
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J = tf.gather(J, tf.constant([0, 1, 2], tf.int32), axis=1)
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T = tf.tensordot(w[0], results, axes=((1), (0)))
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rest_shape_h = tf.concat((v_cloth, tf.ones((v_cloth.shape[0], 1), dtype=tf.float32)), axis=1)
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v = tf.matmul(T, tf.reshape(rest_shape_h, (-1, 4, 1)))
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v = tf.reshape(v, (-1, 4))[:, :3]
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return v, tf.squeeze(J)
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gender = tf.cast(tf.greater_equal(betas[:, 10] , 0), tf.int32)
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T_out, J_out = tf.map_fn(single_run, (R, v_shaped, gender, v_cloth), (tf.float32, tf.float32))
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J0 = tf.reshape(J_out[:,0,:], (-1, 1, 3))
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T_out = tf.subtract(T_out, J0)
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return T_out
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def get_joints_template(self, betas, gender, batch_size):
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v_shaped = tf.tensordot(betas[tf.newaxis,:], self.shapedirs[gender], axes=[[1], [2]]) + tf.reshape(self.v_template[gender], shape=(1,27578,3))
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J = tf.matmul(self.J_regressor[gender], v_shaped)
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J0 = tf.reshape(J[:,0,:], (-1, 1, 3))
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J_single = tf.subtract(J, J0)#[0]
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J = tf.ones((batch_size, 24, 3)) * J_single
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return J
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def run_garment(self, R, betas, gender, v_cloth, filter_idx=None):
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batch_size = v_cloth.shape[0]
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J = self.get_joints_template(betas, gender, batch_size)
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if filter_idx is not None:
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weights = tf.gather(self.weights, filter_idx, axis=1)
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R_cube_big = R
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results = []
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results.append(self.with_zeros(tf.concat((R_cube_big[:,0,:,:], J[:, 0, :, tf.newaxis]), axis=2)))
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for j in range(1, 24):
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results.append(tf.matmul(results[self.parent[j]], self.with_zeros(tf.concat((R_cube_big[:,j,:,:], (J[:, j, :] - J[:, self.parent[j], :])[:,:,tf.newaxis]), axis=2))))
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stacked = tf.stack(results, axis=1)
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results = stacked - self.pack(tf.matmul(stacked, tf.reshape(tf.concat((J, tf.zeros((batch_size, 24, 1), dtype=tf.float32)), axis=2), (batch_size, 24, 4, 1))))
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J = tf.matmul(results, tf.expand_dims(tf.concat([J, tf.ones([batch_size, 24, 1], tf.float32)], axis=2), axis=2), transpose_b=True)
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J = J[:, :, :3, :]
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T = tf.tensordot(weights[gender, :, :], results, axes=((1), (1)))
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T = tf.transpose(T, [1,0,2,3])
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rest_shape_h = tf.concat((v_cloth, tf.ones((batch_size, v_cloth.shape[1], 1), dtype=tf.float32)), axis=2)
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v = tf.matmul(T, tf.reshape(rest_shape_h, (batch_size, -1, 4, 1)))
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v = tf.reshape(v, (batch_size, -1, 4))[:, :, :3]
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J_out = tf.squeeze(J)
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if J.shape[0] == 1:
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J_out = J_out[tf.newaxis, :, :]
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J0 = tf.reshape(J_out[:,0,:], (-1, 1, 3))
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J_out = tf.subtract(J_out, J0)
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T_out = tf.subtract(v, J0)
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return T_out
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def run_garment_with_weights(self, R, betas, v_cloth, weights, J=None):
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if J is None:
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J = self.get_joints_template(betas)
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def single_run(x, parent=self.parent):
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R = x[0]
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J = x[1]
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v_cloth = x[2]
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weights = x[3]
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R_cube_big = R
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results = []
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results.append(self.with_zeros(tf.concat((R_cube_big[0], tf.reshape(J[0, :], (3, 1))), axis=1)))
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for j in range(1, 24):
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results.append(tf.matmul(results[parent[j]], self.with_zeros(tf.concat((R_cube_big[j], tf.reshape(J[j, :] - J[parent[j], :], (3, 1))), axis=1))))
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stacked = tf.stack(results, axis=0)
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results = stacked - self.pack(tf.matmul(stacked, tf.reshape(tf.concat((J, tf.zeros((24, 1), dtype=tf.float32)), axis=1), (24, 4, 1))))
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J = tf.matmul(results, tf.expand_dims(tf.concat([J, tf.ones([24, 1], tf.float32)], axis=1), axis=1), transpose_b=True)
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J = tf.gather(J, tf.constant([0, 1, 2], tf.int32), axis=1)
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T = tf.tensordot(weights, results, axes=((1), (0)))
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rest_shape_h = tf.concat((v_cloth, tf.ones((v_cloth.get_shape().as_list()[0], 1), dtype=tf.float32)), axis=1)
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v = tf.matmul(T, tf.reshape(rest_shape_h, (-1, 4, 1)))
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v = tf.reshape(v, (-1, 4))[:, :3]
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return v, tf.squeeze(J)
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# gender = tf.cast(tf.greater_equal(betas[:, 10] , 0), tf.int32)
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T_out, J_out = tf.map_fn(single_run, (R, J, v_cloth, weights), (tf.float32, tf.float32))
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J0 = tf.reshape(J_out[:,0,:], (-1, 1, 3))
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J_out = tf.subtract(J_out, J0)
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T_out = tf.subtract(T_out, J0)
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return T_out
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def run_backward(self, R, J, v_posed, weights):
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batch_size = R.get_shape()[0]
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J_relative = J[:, 1:, :] - tf.gather(J, tf.constant(list(self.parent.values()), tf.int32), axis=1) # batch_size x 23 x 3
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J_out = []
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J_out.append(tf.expand_dims(J[:, 0, :], axis=-1)) # batch_size x 3 x 1
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results = []
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results.append(tf.expand_dims(tf.eye(3, dtype=tf.float32), axis=0) + tf.zeros((batch_size, 3, 3), dtype=tf.float32))
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for j in range(1, 24):
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results.append(tf.matmul(R[:, self.parent[j], :, :], results[self.parent[j]]))
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J_out.append(tf.matmul(results[j], tf.expand_dims(J_relative[:, j-1, :], axis=-1)) + J_out[self.parent[j]]) # batch_size x 3 x 1
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results = tf.transpose(tf.stack(results, axis=0), [1, 0, 2, 3]) # (batch_size x 24 x 3 x 3)
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J_out = tf.transpose(tf.stack(J_out, axis=0), [1, 0, 2, 3]) # (batch_size x 24 x 3 x 1)
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results = tf.matmul(R, results)
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T = tf.transpose(tf.tensordot(results, weights, axes=((1), (1))), [0, 3, 1, 2]) # (batch_size x 6890 x 3 x 3)
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v_posed = tf.expand_dims(v_posed, axis=-1) # (batch_size x 6890 x 3 x 1)
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J = tf.expand_dims(J, axis=-1) # (batch_size x 24 x 3 x 1)
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v = tf.matmul(T, v_posed) + tf.transpose(tf.tensordot(J_out - tf.matmul(results, J), weights, axes=((1), (1))), [0, 3, 1, 2]) # (batch_size x 6890 x 3 x 1)
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v = tf.squeeze(v, -1) # (batch_size x 6890 x 3)
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J_out = tf.squeeze(J_out, -1) # (batch_size x 24 x 3)
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return v, J_out |