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d8dc31c | 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 | from typing import Optional
import torch
from torch.nn import functional as F
def aa_to_rotmat(theta: torch.Tensor):
"""
Convert axis-angle representation to rotation matrix.
Works by first converting it to a quaternion.
Args:
theta (torch.Tensor): Tensor of shape (B, 3) containing axis-angle representations.
Returns:
torch.Tensor: Corresponding rotation matrices with shape (B, 3, 3).
"""
norm = torch.norm(theta + 1e-8, p = 2, dim = 1)
angle = torch.unsqueeze(norm, -1)
normalized = torch.div(theta, angle)
angle = angle * 0.5
v_cos = torch.cos(angle)
v_sin = torch.sin(angle)
quat = torch.cat([v_cos, v_sin * normalized], dim = 1)
return quat_to_rotmat(quat)
def quat_to_rotmat(quat: torch.Tensor) -> torch.Tensor:
"""
Convert quaternion representation to rotation matrix.
Args:
quat (torch.Tensor) of shape (B, 4); 4 <===> (w, x, y, z).
Returns:
torch.Tensor: Corresponding rotation matrices with shape (B, 3, 3).
"""
norm_quat = quat
norm_quat = norm_quat/norm_quat.norm(p=2, dim=1, keepdim=True)
w, x, y, z = norm_quat[:,0], norm_quat[:,1], norm_quat[:,2], norm_quat[:,3]
B = quat.size(0)
w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2)
wx, wy, wz = w*x, w*y, w*z
xy, xz, yz = x*y, x*z, y*z
rotMat = torch.stack([w2 + x2 - y2 - z2, 2*xy - 2*wz, 2*wy + 2*xz,
2*wz + 2*xy, w2 - x2 + y2 - z2, 2*yz - 2*wx,
2*xz - 2*wy, 2*wx + 2*yz, w2 - x2 - y2 + z2], dim=1).view(B, 3, 3)
return rotMat
def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor:
"""
Converts rotation matrices to 6D rotation representation by Zhou et al. [1]
by dropping the last row. Note that 6D representation is not unique.
Args:
matrix: batch of rotation matrices of size (*, 3, 3)
Returns:
6D rotation representation, of size (*, 6)
[1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
On the Continuity of Rotation Representations in Neural Networks.
IEEE Conference on Computer Vision and Pattern Recognition, 2019.
Retrieved from http://arxiv.org/abs/1812.07035
"""
batch_dim = matrix.size()[:-2]
return matrix[..., :2, :].clone().reshape(batch_dim + (6,))
def rot6d_to_rotmat(x: torch.Tensor) -> torch.Tensor:
"""
Convert 6D rotation representation to 3x3 rotation matrix.
Based on Zhou et al., "On the Continuity of Rotation Representations in Neural Networks", CVPR 2019
Args:
x (torch.Tensor): (B,6) Batch of 6-D rotation representations.
Returns:
torch.Tensor: Batch of corresponding rotation matrices with shape (B,3,3).
"""
x = x.reshape(-1,2,3).permute(0, 2, 1).contiguous()
a1 = x[:, :, 0]
a2 = x[:, :, 1]
b1 = F.normalize(a1)
b2 = F.normalize(a2 - torch.einsum('bi,bi->b', b1, a2).unsqueeze(-1) * b1)
# convert to float32 for type bfloat16
if b1.dtype == torch.bfloat16:
b3 = torch.cross(b1.float(), b2.float(), dim=-1).to(b1.dtype)
else:
b3 = torch.cross(b1, b2, dim=-1)
# return torch.stack((b1, b2, b3), dim=-1) ### Original HMR2.0 model was trained with this line
return torch.stack((b1, b2, b3), dim=-2)
def perspective_projection(points: torch.Tensor,
translation: torch.Tensor,
focal_length: torch.Tensor,
camera_center: Optional[torch.Tensor] = None,
rotation: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
Computes the perspective projection of a set of 3D points.
Args:
points (torch.Tensor): Tensor of shape (B, N, 3) containing the input 3D points.
translation (torch.Tensor): Tensor of shape (B, 3) containing the 3D camera translation.
focal_length (torch.Tensor): Tensor of shape (B, 2) containing the focal length in pixels.
camera_center (torch.Tensor): Tensor of shape (B, 2) containing the camera center in pixels.
rotation (torch.Tensor): Tensor of shape (B, 3, 3) containing the camera rotation.
Returns:
torch.Tensor: Tensor of shape (B, N, 2) containing the projection of the input points.
"""
batch_size = points.shape[0]
if rotation is None:
rotation = torch.eye(3, device=points.device, dtype=points.dtype).unsqueeze(0).expand(batch_size, -1, -1)
if camera_center is None:
camera_center = torch.zeros(batch_size, 2, device=points.device, dtype=points.dtype)
# Populate intrinsic camera matrix K.
K = torch.zeros([batch_size, 3, 3], device=points.device, dtype=points.dtype)
K[:,0,0] = focal_length[:,0]
K[:,1,1] = focal_length[:,1]
K[:,2,2] = 1.
K[:,:-1, -1] = camera_center
# Transform points
points = torch.einsum('bij,bkj->bki', rotation, points)
points = points + translation.unsqueeze(1)
# Apply perspective distortion
projected_points = points / points[:,:,-1].unsqueeze(-1)
# Apply camera intrinsics
projected_points = torch.einsum('bij,bkj->bki', K, projected_points)
return projected_points[:, :, :-1] |