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8b3f571 | 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 | import torch
import numpy as np
from .rotation_conversions import rotation_6d_to_matrix, matrix_to_axis_angle
def accumulate_rotations(relative_rotations):
R_total = [relative_rotations[0]]
for R_rel in relative_rotations[1:]:
R_total.append(np.matmul(R_rel, R_total[-1]))
return np.array(R_total)
def accumulate_rotations_batch(R_rel):
B, T, _, _ = R_rel.shape
R_list = []
R_prev = R_rel[:, 0]
R_list.append(R_prev)
for t in range(1, T):
R_curr = R_rel[:, t] @ R_prev
R_list.append(R_curr)
R_prev = R_curr
R_out = torch.stack(R_list, dim=1)
return R_out
def recover_from_local_position_fast(final_x: torch.Tensor, njoint=22):
"""
final_x: (B, T, D)
return: (B, T, J, 3)
"""
B, T, _ = final_x.shape
device = final_x.device
# -------- unpack --------
positions_no_heading = final_x[:, :, 8:8+3*njoint].reshape(B, T, njoint, 3)
velocities_root_xy = final_x[:, :, :2]
global_heading_diff_rot = final_x[:, :, 2:8]
# -------- rotation --------
R_rel = rotation_6d_to_matrix(global_heading_diff_rot) # (B, T, 3, 3)
R_global = accumulate_rotations_batch(R_rel) # (B, T, 3, 3)
R_inv = R_global.transpose(-1, -2) # inverse
# -------- rotate joints --------
pos = torch.matmul(
R_inv[:, :, None, :, :],
positions_no_heading[..., None]
).squeeze(-1) # (B, T, J, 3)
# -------- root velocity --------
vel_root = torch.zeros((B, T, 3), device=device)
vel_root[:, :, 0] = velocities_root_xy[:, :, 0]
vel_root[:, :, 2] = velocities_root_xy[:, :, 1]
vel_root[:, 1:] = torch.matmul(
R_inv[:, :-1],
vel_root[:, 1:, :, None]
).squeeze(-1)
# -------- integrate --------
root_translation = torch.cumsum(vel_root, dim=1)
pos[:, :, :, 0] += root_translation[:, :, 0:1]
pos[:, :, :, 2] += root_translation[:, :, 2:3]
return pos
def recover_from_local_position(final_x, njoint):
if final_x.ndim == 3:
bs, nfrm, _ = final_x.shape
is_batched = True
else:
nfrm, _ = final_x.shape
bs = 1
is_batched = False
final_x = final_x.reshape(1, *final_x.shape)
positions_no_heading = final_x[:,:,8:8+3*njoint].reshape(bs, nfrm, njoint, 3)
velocities_root_xy_no_heading = final_x[:,:,:2]
global_heading_diff_rot = final_x[:,:,2:8]
positions_with_heading = []
for b in range(bs):
global_heading_rot = accumulate_rotations(rotation_6d_to_matrix(torch.from_numpy(global_heading_diff_rot[b])).numpy())
inv_global_heading_rot = np.transpose(global_heading_rot, (0, 2, 1))
curr_pos_with_heading = np.matmul(np.repeat(inv_global_heading_rot[:, None,:, :], njoint, axis=1),
positions_no_heading[b][...,None]).squeeze(-1)
velocities_root_xyz_no_heading = np.zeros((velocities_root_xy_no_heading[b].shape[0], 3))
velocities_root_xyz_no_heading[:, 0] = velocities_root_xy_no_heading[b, :, 0]
velocities_root_xyz_no_heading[:, 2] = velocities_root_xy_no_heading[b, :, 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)
curr_pos_with_heading[:, :, 0] += root_translation[:, 0:1]
curr_pos_with_heading[:, :, 2] += root_translation[:, 2:]
positions_with_heading.append(curr_pos_with_heading)
positions_with_heading = np.stack(positions_with_heading, axis=0)
if not is_batched:
positions_with_heading = positions_with_heading.squeeze(0)
return positions_with_heading
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
nfrm, njoint, _, _ = rotations_matrix.shape
axis_angle = matrix_to_axis_angle(torch.from_numpy(rotations_matrix)).numpy().reshape(nfrm, -1)
global_orient = axis_angle[:, :3]
# y-up -> z-up
# global_orient_mat = torch.from_numpy(rotations_matrix)[:, 0].float()
# rotation_matrix = torch.tensor([[1, 0, 0], [0, 0, 1], [0, -1, 0]]).float()
# global_orient_mat = torch.einsum('ij,tjk->tik', rotation_matrix, global_orient_mat)
# global_orient = matrix_to_axis_angle(global_orient_mat)
# root_translation = torch.einsum('ij,tj->ti', rotation_matrix, torch.from_numpy(root_translation).float())
return dict(
global_orient = global_orient,
body_pose = axis_angle[:, 3:],
transl = root_translation,
betas = torch.zeros((nfrm, 10))
)
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