Hugging Face's logo

zirobtc
/
motion-stream

TensorBoard
Model card Files
xet
Metrics Community
motion-stream
File size: 8,380 Bytes 
0e267a7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
 
# Reference: https://github.com/Li-xingXiao/272-dim-Motion-Representation

# representation: 272 dim
# :2 local xz velocities of root, no heading, can recover translation
# 2:8  heading angular velocities, 6d rotation, can recover heading
# 8:8+3*njoint local position, no heading, all at xz origin
# 8+3*njoint:8+6*njoint local velocities, no heading, all at xz origin, can recover local postion
# 8+6*njoint:8+12*njoint local rotations, 6d rotation, no heading, all frames z+

import numpy as np
from utils.face_z_align_util import rotation_6d_to_matrix, matrix_to_axis_angle
import copy
import torch
import os
import visualization.plot_3d_global as plot_3d
import argparse
import tqdm
# from visualization.smplx2joints import process_smplx_data

def findAllFile(base, endswith='.npy'):
    file_path = []
    for root, ds, fs in os.walk(base, followlinks=True):
        for f in fs:
            fullname = os.path.join(root, f)
            if fullname.endswith(endswith):
                file_path.append(fullname)
    return file_path

def rot_yaw(yaw):
    cs = np.cos(yaw)
    sn = np.sin(yaw)
    return np.array([[cs,0,sn],[0,1,0],[-sn,0,cs]])


def my_quat_rotate(q, v):
    shape = q.shape
    q_w = q[:, -1]
    q_vec = q[:, :3]
    a = v * (2.0 * q_w ** 2 - 1.0).unsqueeze(-1)
    b = torch.cross(q_vec, v, dim=-1) * q_w.unsqueeze(-1) * 2.0
    c = q_vec * \
        torch.bmm(q_vec.view(shape[0], 1, 3), v.view(
            shape[0], 3, 1)).squeeze(-1) * 2.0
    return a + b + c

    
def calc_heading(q):
    ref_dir = torch.zeros_like(q[..., 0:3])
    ref_dir[..., 2] = 1
    rot_dir = my_quat_rotate(q, ref_dir)

    heading = torch.atan2(rot_dir[..., 0], rot_dir[..., 2])
    return heading


def calc_heading_quat_inv(q):
    heading = calc_heading(q)
    axis = torch.zeros_like(q[..., 0:3])
    axis[..., 1] = 1

    return -heading, axis

def accumulate_rotations(relative_rotations):
    """Accumulate relative rotations to get the overall rotation"""
    # Initial rotation is the rotation matrix
    R_total = [relative_rotations[0]]
    # Iterate through all relative rotations, accumulating them
    for R_rel in relative_rotations[1:]:
        R_total.append(np.matmul(R_rel, R_total[-1]))
    
    return np.array(R_total)

def recover_from_local_position(final_x, njoint):
    # take positions_no_heading: local position on xz ori, no heading
    # velocities_root_xy_no_heading: to recover translation
    # global_heading_diff_rot: to recover root rotation
    nfrm, _ = final_x.shape
    positions_no_heading = final_x[:,8:8+3*njoint].reshape(nfrm, -1, 3) # frames, njoints * 3
    velocities_root_xy_no_heading = final_x[:,:2] # frames, 2
    global_heading_diff_rot = final_x[:,2:8] # frames, 6

    # recover global heading
    global_heading_rot = accumulate_rotations(rotation_6d_to_matrix(torch.from_numpy(global_heading_diff_rot)).numpy())
    inv_global_heading_rot = np.transpose(global_heading_rot, (0, 2, 1))
    # add global heading to position
    positions_with_heading = np.matmul(np.repeat(inv_global_heading_rot[:, None,:, :], njoint, axis=1), positions_no_heading[...,None]).squeeze(-1)

    # recover root translation
    # add heading to velocities_root_xy_no_heading

    velocities_root_xyz_no_heading = np.zeros((velocities_root_xy_no_heading.shape[0], 3))
    velocities_root_xyz_no_heading[:, 0] = velocities_root_xy_no_heading[:, 0]
    velocities_root_xyz_no_heading[:, 2] = velocities_root_xy_no_heading[:, 1]
    velocities_root_xyz_no_heading[1:, :] = np.matmul(inv_global_heading_rot[:-1], velocities_root_xyz_no_heading[1:, :,None]).squeeze(-1)

    root_translation = np.cumsum(velocities_root_xyz_no_heading, axis=0)

    # add root translation
    positions_with_heading[:, :, 0] += root_translation[:, 0:1]
    positions_with_heading[:, :, 2] += root_translation[:, 2:]

    return positions_with_heading


# add hip height to translation when recoverring from rotation
def recover_from_local_rotation(final_x, njoint):
    nfrm, _ = final_x.shape
    rotations_matrix = rotation_6d_to_matrix(torch.from_numpy(final_x[:,8+6*njoint:8+12*njoint]).reshape(nfrm, -1, 6)).numpy()
    global_heading_diff_rot = final_x[:,2:8]
    velocities_root_xy_no_heading = final_x[:,:2]
    positions_no_heading = final_x[:, 8:8+3*njoint].reshape(nfrm, -1, 3)
    height = positions_no_heading[:, 0, 1]

    global_heading_rot = accumulate_rotations(rotation_6d_to_matrix(torch.from_numpy(global_heading_diff_rot)).numpy())
    inv_global_heading_rot = np.transpose(global_heading_rot, (0, 2, 1))
    # recover root rotation
    rotations_matrix[:,0,...] = np.matmul(inv_global_heading_rot, rotations_matrix[:,0,...])
    velocities_root_xyz_no_heading = np.zeros((velocities_root_xy_no_heading.shape[0], 3))
    velocities_root_xyz_no_heading[:, 0] = velocities_root_xy_no_heading[:, 0]
    velocities_root_xyz_no_heading[:, 2] = velocities_root_xy_no_heading[:, 1]
    velocities_root_xyz_no_heading[1:, :] = np.matmul(inv_global_heading_rot[:-1], velocities_root_xyz_no_heading[1:, :,None]).squeeze(-1)
    root_translation = np.cumsum(velocities_root_xyz_no_heading, axis=0)
    root_translation[:, 1] = height
    smpl_85 = rotations_matrix_to_smpl85(rotations_matrix, root_translation)
    return smpl_85

def rotations_matrix_to_smpl85(rotations_matrix, translation):
    nfrm, njoint, _, _ = rotations_matrix.shape
    axis_angle = matrix_to_axis_angle(torch.from_numpy(rotations_matrix)).numpy().reshape(nfrm, -1)
    smpl_85 = np.concatenate([axis_angle, np.zeros((nfrm, 6)), translation, np.zeros((nfrm, 10))], axis=-1)
    return smpl_85




def smpl85_2_smpl322(smpl_85_data):
    result = np.concatenate((smpl_85_data[:,:66], np.zeros((smpl_85_data.shape[0], 90)), np.zeros((smpl_85_data.shape[0], 3)), np.zeros((smpl_85_data.shape[0], 50)), np.zeros((smpl_85_data.shape[0], 100)), smpl_85_data[:,72:72+3], smpl_85_data[:,75:]), axis=-1)
    return result

def visualize_smpl_85(data, title=None, output_path='visualize_result', name='', fps=30):
    # data: torch.Size([nframe, 85])
    smpl_85_data = data
    if len(smpl_85_data.shape) == 3:
       smpl_85_data = np.squeeze(smpl_85_data, axis=0)
    
    smpl_85_data = smpl85_2_smpl322(smpl_85_data)
    vert, joints, motion, faces = process_smplx_data(smpl_85_data, norm_global_orient=False, transform=False)
    xyz = joints[:, :22, :].reshape(1, -1, 22, 3).detach().cpu().numpy()
    os.makedirs(os.path.dirname(output_path), exist_ok=True)
    pose_vis = plot_3d.draw_to_batch(xyz, title_batch=title, outname=[f'{output_path}/rot_{name}.mp4'], fps=fps)
    return output_path


def visualize_pos_xyz(xyz, title_batch=None, output_path='./', name='', fps=30):
    # xyz: torch.Size([nframe, 22, 3])
    xyz = xyz[:1]   
    bs, seq = xyz.shape[:2]
    xyz = xyz.reshape(bs, seq, -1, 3)
    os.makedirs(os.path.dirname(output_path), exist_ok=True)
    plot_xyz = plot_3d.draw_to_batch(xyz, title_batch, [f'{output_path}/pos_{name}.mp4'], fps=fps)
    return output_path


if __name__ == '__main__':
    njoint = 22
    parser = argparse.ArgumentParser(description='Visualize new representation.')
    parser.add_argument('--input_dir', type=str, required=True, help='Input path')
    parser.add_argument('--mode', type=str, required=True, default='rot', choices=['rot', 'pos'], help='Recover from rotation or position')
    parser.add_argument('--output_dir', type=str, required=True, help='Output path')
    args = parser.parse_args()
    os.makedirs(args.output_dir, exist_ok=True)
    
    for data_path in tqdm.tqdm(findAllFile(args.input_dir, endswith='.npy')):
        data_272 = np.load(data_path)
        if args.mode == 'rot':
            # recover from rotation
            global_rotation = recover_from_local_rotation(data_272, njoint)  # get the 85-dim smpl data
            visualize_smpl_85(global_rotation, output_path=args.output_dir, name=data_path.split('/')[-1].split('.')[0])
            print(f"Visualized results are saved in {args.output_dir}")
        else:
            # recover from position
            global_position = recover_from_local_position(data_272, njoint)
            global_position = np.expand_dims(global_position, axis=0)
            visualize_pos_xyz(global_position, output_path=args.output_dir, name=data_path.split('/')[-1].split('.')[0])
            print(f"Visualized results are saved in {args.output_dir}")