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from scipy.ndimage import gaussian_filter1d
import numpy as np
import json
import os
import torch
def preprocess_test(motion, meanpose, stdpose, unit=128):
motion = motion * unit
motion[1, :, :] = (motion[2, :, :] + motion[5, :, :]) / 2
motion[8, :, :] = (motion[9, :, :] + motion[12, :, :]) / 2
start = motion[8, :, 0]
motion = localize_motion(motion)
motion = normalize_motion(motion, meanpose, stdpose)
return motion, start
def postprocess(motion, meanpose, stdpose, unit=128, start=None):
motion = motion.detach().cpu().numpy()[0].reshape(-1, 2, motion.shape[-1])
motion = normalize_motion_inv(motion, meanpose, stdpose)
motion = globalize_motion(motion, start=start)
# motion = motion / unit
return motion
def preprocess_mixamo(motion, unit=128):
_, D, _ = motion.shape
horizontal_dim = 0
vertical_dim = D - 1
motion[1, :, :] = (motion[2, :, :] + motion[5, :, :]) / 2
motion[8, :, :] = (motion[9, :, :] + motion[12, :, :]) / 2
# rotate 180
motion[:, horizontal_dim, :] = - motion[:, horizontal_dim, :]
motion[:, vertical_dim, :] = - motion[:, vertical_dim, :]
motion = motion * unit
return motion
def rotate_motion_3d(motion3d, change_of_basis):
if change_of_basis is not None: motion3d = change_of_basis @ motion3d
return motion3d
def limb_scale_motion_2d(motion2d, global_range, local_range):
global_scale = global_range[0] + np.random.random() * (global_range[1] - global_range[0])
local_scales = local_range[0] + np.random.random([8]) * (local_range[1] - local_range[0])
motion_scale = scale_limbs(motion2d, global_scale, local_scales)
return motion_scale
def localize_motion(motion):
"""
Motion fed into our network is the local motion, i.e. coordinates relative to the hip joint.
This function removes global motion of the hip joint, and instead represents global motion with velocity
"""
D = motion.shape[1]
# subtract centers to local coordinates
centers = motion[8, :, :] # N_dim x T
motion = motion - centers
# adding velocity
translation = centers[:, 1:] - centers[:, :-1]
velocity = np.c_[np.zeros((D, 1)), translation]
velocity = velocity.reshape(1, D, -1)
motion = np.r_[motion[:8], motion[9:], velocity]
# motion_proj = np.r_[motion_proj[:8], motion_proj[9:]]
return motion
def globalize_motion(motion, start=None, velocity=None):
"""
inverse process of localize_motion
"""
if velocity is None: velocity = motion[-1].copy()
motion_inv = np.r_[motion[:8], np.zeros((1, 2, motion.shape[-1])), motion[8:-1]]
# restore centre position
centers = np.zeros_like(velocity)
sum = 0
for i in range(motion.shape[-1]):
sum += velocity[:, i]
centers[:, i] = sum
centers += start.reshape([2, 1])
return motion_inv + centers.reshape((1, 2, -1))
def normalize_motion(motion, meanpose, stdpose):
"""
:param motion: (J, 2, T)
:param meanpose: (J, 2)
:param stdpose: (J, 2)
:return:
"""
if motion.shape[1] == 2 and meanpose.shape[1] == 3:
meanpose = meanpose[:, [0, 2]]
if motion.shape[1] == 2 and stdpose.shape[1] == 3:
stdpose = stdpose[:, [0, 2]]
return (motion - meanpose[:, :, np.newaxis]) / stdpose[:, :, np.newaxis]
def normalize_motion_inv(motion, meanpose, stdpose):
if motion.shape[1] == 2 and meanpose.shape[1] == 3:
meanpose = meanpose[:, [0, 2]]
if motion.shape[1] == 2 and stdpose.shape[1] == 3:
stdpose = stdpose[:, [0, 2]]
return motion * stdpose[:, :, np.newaxis] + meanpose[:, :, np.newaxis]
def get_change_of_basis(motion3d, angles=None):
"""
Get the unit vectors for local rectangular coordinates for given 3D motion
:param motion3d: numpy array. 3D motion from 3D joints positions, shape (nr_joints, 3, nr_frames).
:param angles: tuple of length 3. Rotation angles around each axis.
:return: numpy array. unit vectors for local rectangular coordinates's , shape (3, 3).
"""
# 2 RightArm 5 LeftArm 9 RightUpLeg 12 LeftUpLeg
horizontal = (motion3d[2] - motion3d[5] + motion3d[9] - motion3d[12]) / 2
horizontal = np.mean(horizontal, axis=1)
horizontal = horizontal / np.linalg.norm(horizontal)
local_z = np.array([0, 0, 1])
local_y = np.cross(horizontal, local_z) # bugs!!!, horizontal and local_Z may not be perpendicular
local_y = local_y / np.linalg.norm(local_y)
local_x = np.cross(local_y, local_z)
local = np.stack([local_x, local_y, local_z], axis=0)
if angles is not None:
local = rotate_basis(local, angles)
return local
def rotate_basis(local3d, angles):
"""
Rotate local rectangular coordinates from given view_angles.
:param local3d: numpy array. Unit vectors for local rectangular coordinates's , shape (3, 3).
:param angles: tuple of length 3. Rotation angles around each axis.
:return:
"""
cx, cy, cz = np.cos(angles)
sx, sy, sz = np.sin(angles)
x = local3d[0]
x_cpm = np.array([
[0, -x[2], x[1]],
[x[2], 0, -x[0]],
[-x[1], x[0], 0]
], dtype='float')
x = x.reshape(-1, 1)
mat33_x = cx * np.eye(3) + sx * x_cpm + (1.0 - cx) * np.matmul(x, x.T)
mat33_z = np.array([
[cz, sz, 0],
[-sz, cz, 0],
[0, 0, 1]
], dtype='float')
local3d = local3d @ mat33_x.T @ mat33_z
return local3d
def get_foot_vel(batch_motion, foot_idx):
return batch_motion[:, foot_idx, 1:] - batch_motion[:, foot_idx, :-1] + batch_motion[:, -2:, 1:].repeat(1, 2, 1)
def get_limbs(motion):
J, D, T = motion.shape
limbs = np.zeros([14, D, T])
limbs[0] = motion[0] - motion[1] # neck
limbs[1] = motion[2] - motion[1] # r_shoulder
limbs[2] = motion[3] - motion[2] # r_arm
limbs[3] = motion[4] - motion[3] # r_forearm
limbs[4] = motion[5] - motion[1] # l_shoulder
limbs[5] = motion[6] - motion[5] # l_arm
limbs[6] = motion[7] - motion[6] # l_forearm
limbs[7] = motion[1] - motion[8] # spine
limbs[8] = motion[9] - motion[8] # r_pelvis
limbs[9] = motion[10] - motion[9] # r_thigh
limbs[10] = motion[11] - motion[10] # r_shin
limbs[11] = motion[12] - motion[8] # l_pelvis
limbs[12] = motion[13] - motion[12] # l_thigh
limbs[13] = motion[14] - motion[13] # l_shin
return limbs
def scale_limbs(motion, global_scale, local_scales):
"""
:param motion: joint sequence [J, 2, T]
:param local_scales: 8 numbers of scales
:return: scaled joint sequence
"""
limb_dependents = [
[0],
[2, 3, 4],
[3, 4],
[4],
[5, 6, 7],
[6, 7],
[7],
[0, 1, 2, 3, 4, 5, 6, 7],
[9, 10, 11],
[10, 11],
[11],
[12, 13, 14],
[13, 14],
[14]
]
limbs = get_limbs(motion)
scaled_limbs = limbs.copy() * global_scale
scaled_limbs[0] *= local_scales[0]
scaled_limbs[1] *= local_scales[1]
scaled_limbs[2] *= local_scales[2]
scaled_limbs[3] *= local_scales[3]
scaled_limbs[4] *= local_scales[1]
scaled_limbs[5] *= local_scales[2]
scaled_limbs[6] *= local_scales[3]
scaled_limbs[7] *= local_scales[4]
scaled_limbs[8] *= local_scales[5]
scaled_limbs[9] *= local_scales[6]
scaled_limbs[10] *= local_scales[7]
scaled_limbs[11] *= local_scales[5]
scaled_limbs[12] *= local_scales[6]
scaled_limbs[13] *= local_scales[7]
delta = scaled_limbs - limbs
scaled_motion = motion.copy()
scaled_motion[limb_dependents[7]] += delta[7] # spine
scaled_motion[limb_dependents[1]] += delta[1] # r_shoulder
scaled_motion[limb_dependents[4]] += delta[4] # l_shoulder
scaled_motion[limb_dependents[2]] += delta[2] # r_arm
scaled_motion[limb_dependents[5]] += delta[5] # l_arm
scaled_motion[limb_dependents[3]] += delta[3] # r_forearm
scaled_motion[limb_dependents[6]] += delta[6] # l_forearm
scaled_motion[limb_dependents[0]] += delta[0] # neck
scaled_motion[limb_dependents[8]] += delta[8] # r_pelvis
scaled_motion[limb_dependents[11]] += delta[11] # l_pelvis
scaled_motion[limb_dependents[9]] += delta[9] # r_thigh
scaled_motion[limb_dependents[12]] += delta[12] # l_thigh
scaled_motion[limb_dependents[10]] += delta[10] # r_shin
scaled_motion[limb_dependents[13]] += delta[13] # l_shin
return scaled_motion
def get_limb_lengths(x):
_, dims, _ = x.shape
if dims == 2:
limbs = np.max(np.linalg.norm(get_limbs(x), axis=1), axis=-1)
limb_lengths = np.array([
limbs[0], # neck
max(limbs[1], limbs[4]), # shoulders
max(limbs[2], limbs[5]), # arms
max(limbs[3], limbs[6]), # forearms
limbs[7], # spine
max(limbs[8], limbs[11]), # pelvis
max(limbs[9], limbs[12]), # thighs
max(limbs[10], limbs[13]) # shins
])
else:
limbs = np.mean(np.linalg.norm(get_limbs(x), axis=1), axis=-1)
limb_lengths = np.array([
limbs[0], # neck
(limbs[1] + limbs[4]) / 2., # shoulders
(limbs[2] + limbs[5]) / 2., # arms
(limbs[3] + limbs[6]) / 2., # forearms
limbs[7], # spine
(limbs[8] + limbs[11]) / 2., # pelvis
(limbs[9] + limbs[12]) / 2., # thighs
(limbs[10] + limbs[13]) / 2. # shins
])
return limb_lengths
def limb_norm(x_a, x_b):
limb_lengths_a = get_limb_lengths(x_a)
limb_lengths_b = get_limb_lengths(x_b)
limb_lengths_a[limb_lengths_a < 1e-3] = 1e-3
local_scales = limb_lengths_b / limb_lengths_a
x_ab = scale_limbs(x_a, global_scale=1.0, local_scales=local_scales)
return x_ab
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