import numbers
import numpy as np
import torch
from einops.einops import rearrange
from torch.nn import functional as F
"""
Useful geometric operations, e.g. Perspective projection and a differentiable Rodrigues formula
Parts of the code are taken from https://github.com/MandyMo/pytorch_HMR
"""
def batch_rodrigues(theta):
"""Convert axis-angle representation to rotation matrix.
Args:
theta: size = [B, 3]
Returns:
Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]
"""
l1norm = torch.norm(theta + 1e-8, p=2, dim=1)
angle = torch.unsqueeze(l1norm, -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):
"""Convert quaternion coefficients to rotation matrix.
Args:
quat: size = [B, 4] 4 <===>(w, x, y, z)
Returns:
Rotation matrix corresponding to the quaternion -- size = [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 rotation_matrix_to_angle_axis(rotation_matrix):
"""
This function is borrowed from https://github.com/kornia/kornia
Convert 3x4 rotation matrix to Rodrigues vector
Args:
rotation_matrix (Tensor): rotation matrix.
Returns:
Tensor: Rodrigues vector transformation.
Shape:
- Input: :math:`(N, 3, 4)`
- Output: :math:`(N, 3)`
Example:
>>> input = torch.rand(2, 3, 4) # Nx4x4
>>> output = tgm.rotation_matrix_to_angle_axis(input) # Nx3
"""
if rotation_matrix.shape[1:] == (3, 3):
rot_mat = rotation_matrix.reshape(-1, 3, 3)
hom = torch.tensor([0, 0, 1], dtype=torch.float32, device=rotation_matrix.device).reshape(
1, 3, 1
).expand(rot_mat.shape[0], -1, -1)
rotation_matrix = torch.cat([rot_mat, hom], dim=-1)
quaternion = rotation_matrix_to_quaternion(rotation_matrix)
aa = quaternion_to_angle_axis(quaternion)
aa[torch.isnan(aa)] = 0.0
return aa
def quaternion_to_angle_axis(quaternion: torch.Tensor) -> torch.Tensor:
"""
This function is borrowed from https://github.com/kornia/kornia
Convert quaternion vector to angle axis of rotation.
Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h
Args:
quaternion (torch.Tensor): tensor with quaternions.
Return:
torch.Tensor: tensor with angle axis of rotation.
Shape:
- Input: :math:`(*, 4)` where `*` means, any number of dimensions
- Output: :math:`(*, 3)`
Example:
>>> quaternion = torch.rand(2, 4) # Nx4
>>> angle_axis = tgm.quaternion_to_angle_axis(quaternion) # Nx3
"""
if not torch.is_tensor(quaternion):
raise TypeError("Input type is not a torch.Tensor. Got {}".format(type(quaternion)))
if not quaternion.shape[-1] == 4:
raise ValueError(
"Input must be a tensor of shape Nx4 or 4. Got {}".format(quaternion.shape)
)
# unpack input and compute conversion
q1: torch.Tensor = quaternion[..., 1]
q2: torch.Tensor = quaternion[..., 2]
q3: torch.Tensor = quaternion[..., 3]
sin_squared_theta: torch.Tensor = q1 * q1 + q2 * q2 + q3 * q3
sin_theta: torch.Tensor = torch.sqrt(sin_squared_theta)
cos_theta: torch.Tensor = quaternion[..., 0]
two_theta: torch.Tensor = 2.0 * torch.where(
cos_theta < 0.0, torch.atan2(-sin_theta, -cos_theta), torch.atan2(sin_theta, cos_theta)
)
k_pos: torch.Tensor = two_theta / sin_theta
k_neg: torch.Tensor = 2.0 * torch.ones_like(sin_theta)
k: torch.Tensor = torch.where(sin_squared_theta > 0.0, k_pos, k_neg)
angle_axis: torch.Tensor = torch.zeros_like(quaternion)[..., :3]
angle_axis[..., 0] += q1 * k
angle_axis[..., 1] += q2 * k
angle_axis[..., 2] += q3 * k
return angle_axis
def quaternion_to_angle(quaternion: torch.Tensor) -> torch.Tensor:
"""
Convert quaternion vector to angle of the rotation.
Args:
quaternion (torch.Tensor): tensor with quaternions.
Return:
torch.Tensor: tensor with angle axis of rotation.
Shape:
- Input: :math:`(*, 4)` where `*` means, any number of dimensions
- Output: :math:`(*, 1)`
Example:
>>> quaternion = torch.rand(2, 4) # Nx4
>>> angle_axis = tgm.quaternion_to_angle(quaternion) # Nx1
"""
if not torch.is_tensor(quaternion):
raise TypeError("Input type is not a torch.Tensor. Got {}".format(type(quaternion)))
if not quaternion.shape[-1] == 4:
raise ValueError(
"Input must be a tensor of shape Nx4 or 4. Got {}".format(quaternion.shape)
)
# unpack input and compute conversion
q1: torch.Tensor = quaternion[..., 1]
q2: torch.Tensor = quaternion[..., 2]
q3: torch.Tensor = quaternion[..., 3]
sin_squared_theta: torch.Tensor = q1 * q1 + q2 * q2 + q3 * q3
sin_theta: torch.Tensor = torch.sqrt(sin_squared_theta)
cos_theta: torch.Tensor = quaternion[..., 0]
theta: torch.Tensor = 2.0 * torch.where(
cos_theta < 0.0, torch.atan2(-sin_theta, -cos_theta), torch.atan2(sin_theta, cos_theta)
)
# theta: torch.Tensor = 2.0 * torch.atan2(sin_theta, cos_theta)
# theta2 = torch.where(sin_squared_theta > 0.0, - theta, theta)
return theta.unsqueeze(-1)
def rotation_matrix_to_quaternion(rotation_matrix, eps=1e-6):
"""
This function is borrowed from https://github.com/kornia/kornia
Convert 3x4 rotation matrix to 4d quaternion vector
This algorithm is based on algorithm described in
https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py#L201
Args:
rotation_matrix (Tensor): the rotation matrix to convert.
Return:
Tensor: the rotation in quaternion
Shape:
- Input: :math:`(N, 3, 4)`
- Output: :math:`(N, 4)`
Example:
>>> input = torch.rand(4, 3, 4) # Nx3x4
>>> output = tgm.rotation_matrix_to_quaternion(input) # Nx4
"""
if not torch.is_tensor(rotation_matrix):
raise TypeError("Input type is not a torch.Tensor. Got {}".format(type(rotation_matrix)))
if len(rotation_matrix.shape) > 3:
raise ValueError(
"Input size must be a three dimensional tensor. Got {}".format(rotation_matrix.shape)
)
# if not rotation_matrix.shape[-2:] == (3, 4):
# raise ValueError(
# "Input size must be a N x 3 x 4 tensor. Got {}".format(
# rotation_matrix.shape))
rmat_t = torch.transpose(rotation_matrix, 1, 2)
mask_d2 = rmat_t[:, 2, 2] < eps
mask_d0_d1 = rmat_t[:, 0, 0] > rmat_t[:, 1, 1]
mask_d0_nd1 = rmat_t[:, 0, 0] < -rmat_t[:, 1, 1]
t0 = 1 + rmat_t[:, 0, 0] - rmat_t[:, 1, 1] - rmat_t[:, 2, 2]
q0 = torch.stack([
rmat_t[:, 1, 2] - rmat_t[:, 2, 1], t0, rmat_t[:, 0, 1] + rmat_t[:, 1, 0],
rmat_t[:, 2, 0] + rmat_t[:, 0, 2]
], -1)
t0_rep = t0.repeat(4, 1).t()
t1 = 1 - rmat_t[:, 0, 0] + rmat_t[:, 1, 1] - rmat_t[:, 2, 2]
q1 = torch.stack([
rmat_t[:, 2, 0] - rmat_t[:, 0, 2], rmat_t[:, 0, 1] + rmat_t[:, 1, 0], t1,
rmat_t[:, 1, 2] + rmat_t[:, 2, 1]
], -1)
t1_rep = t1.repeat(4, 1).t()
t2 = 1 - rmat_t[:, 0, 0] - rmat_t[:, 1, 1] + rmat_t[:, 2, 2]
q2 = torch.stack([
rmat_t[:, 0, 1] - rmat_t[:, 1, 0], rmat_t[:, 2, 0] + rmat_t[:, 0, 2],
rmat_t[:, 1, 2] + rmat_t[:, 2, 1], t2
], -1)
t2_rep = t2.repeat(4, 1).t()
t3 = 1 + rmat_t[:, 0, 0] + rmat_t[:, 1, 1] + rmat_t[:, 2, 2]
q3 = torch.stack([
t3, rmat_t[:, 1, 2] - rmat_t[:, 2, 1], rmat_t[:, 2, 0] - rmat_t[:, 0, 2],
rmat_t[:, 0, 1] - rmat_t[:, 1, 0]
], -1)
t3_rep = t3.repeat(4, 1).t()
mask_c0 = mask_d2 * mask_d0_d1
mask_c1 = mask_d2 * ~mask_d0_d1
mask_c2 = ~mask_d2 * mask_d0_nd1
mask_c3 = ~mask_d2 * ~mask_d0_nd1
mask_c0 = mask_c0.view(-1, 1).type_as(q0)
mask_c1 = mask_c1.view(-1, 1).type_as(q1)
mask_c2 = mask_c2.view(-1, 1).type_as(q2)
mask_c3 = mask_c3.view(-1, 1).type_as(q3)
q = q0 * mask_c0 + q1 * mask_c1 + q2 * mask_c2 + q3 * mask_c3
q /= torch.sqrt(
t0_rep * mask_c0 + t1_rep * mask_c1 + # noqa
t2_rep * mask_c2 + t3_rep * mask_c3
) # noqa
q *= 0.5
return q
def batch_euler2matrix(r):
return quaternion_to_rotation_matrix(euler_to_quaternion(r))
def euler_to_quaternion(r):
x = r[..., 0]
y = r[..., 1]
z = r[..., 2]
z = z / 2.0
y = y / 2.0
x = x / 2.0
cz = torch.cos(z)
sz = torch.sin(z)
cy = torch.cos(y)
sy = torch.sin(y)
cx = torch.cos(x)
sx = torch.sin(x)
quaternion = torch.zeros_like(r.repeat(1, 2))[..., :4].to(r.device)
quaternion[..., 0] += cx * cy * cz - sx * sy * sz
quaternion[..., 1] += cx * sy * sz + cy * cz * sx
quaternion[..., 2] += cx * cz * sy - sx * cy * sz
quaternion[..., 3] += cx * cy * sz + sx * cz * sy
return quaternion
def quaternion_to_rotation_matrix(quat):
"""Convert quaternion coefficients to rotation matrix.
Args:
quat: size = [B, 4] 4 <===>(w, x, y, z)
Returns:
Rotation matrix corresponding to the quaternion -- size = [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 rot6d_to_rotmat(x):
"""Convert 6D rotation representation to 3x3 rotation matrix.
Based on Zhou et al., "On the Continuity of Rotation Representations in Neural Networks", CVPR 2019
Input:
(B,6) Batch of 6-D rotation representations
Output:
(B,3,3) Batch of corresponding rotation matrices
"""
if x.shape[-1] == 6:
batch_size = x.shape[0]
if len(x.shape) == 3:
num = x.shape[1]
x = rearrange(x, 'b n d -> (b n) d', d=6)
else:
num = 1
x = rearrange(x, 'b (k l) -> b k l', k=3, l=2)
# x = x.view(-1,3,2)
a1 = x[:, :, 0]
a2 = x[:, :, 1]
b1 = F.normalize(a1)
b2 = F.normalize(a2 - torch.einsum('bi,bi->b', b1, a2).unsqueeze(-1) * b1)
b3 = torch.cross(b1, b2, dim=-1)
mat = torch.stack((b1, b2, b3), dim=-1)
if num > 1:
mat = rearrange(mat, '(b n) h w-> b n h w', b=batch_size, n=num, h=3, w=3)
else:
x = x.view(-1, 3, 2)
a1 = x[:, :, 0]
a2 = x[:, :, 1]
b1 = F.normalize(a1)
b2 = F.normalize(a2 - torch.einsum('bi,bi->b', b1, a2).unsqueeze(-1) * b1)
b3 = torch.cross(b1, b2, dim=-1)
mat = torch.stack((b1, b2, b3), dim=-1)
return mat
def rotmat_to_rot6d(x):
"""Convert 3x3 rotation matrix to 6D rotation representation.
Based on Zhou et al., "On the Continuity of Rotation Representations in Neural Networks", CVPR 2019
Input:
(B,3,3) Batch of corresponding rotation matrices
Output:
(B,6) Batch of 6-D rotation representations
"""
batch_size = x.shape[0]
x = x[:, :, :2]
x = x.reshape(batch_size, 6)
return x
def rotmat_to_angle(x):
"""Convert rotation to one-D angle.
Based on Zhou et al., "On the Continuity of Rotation Representations in Neural Networks", CVPR 2019
Input:
(B,2) Batch of corresponding rotation
Output:
(B,1) Batch of 1-D angle
"""
a = F.normalize(x)
angle = torch.atan2(a[:, 0], a[:, 1]).unsqueeze(-1)
return angle
def projection(pred_joints, pred_camera, retain_z=False, iwp_mode=True):
""" Project 3D points on the image plane based on the given camera info,
Identity rotation and Weak Perspective (IWP) camera is used when iwp_mode=True, more about camera settings:
SPEC: Seeing People in the Wild with an Estimated Camera, ICCV 2021
"""
batch_size = pred_joints.shape[0]
if iwp_mode:
cam_sxy = pred_camera['cam_sxy']
pred_cam_t = torch.stack([
cam_sxy[:, 1], cam_sxy[:, 2], 2 * 5000. / (224. * cam_sxy[:, 0] + 1e-9)
],
dim=-1)
camera_center = torch.zeros(batch_size, 2)
pred_keypoints_2d = perspective_projection(
pred_joints,
rotation=torch.eye(3).unsqueeze(0).expand(batch_size, -1, -1).to(pred_joints.device),
translation=pred_cam_t,
focal_length=5000.,
camera_center=camera_center,
retain_z=retain_z
)
# # Normalize keypoints to [-1,1]
# pred_keypoints_2d = pred_keypoints_2d / (224. / 2.)
else:
assert type(pred_camera) is dict
bbox_scale, bbox_center = pred_camera['bbox_scale'], pred_camera['bbox_center']
img_w, img_h, crop_res = pred_camera['img_w'], pred_camera['img_h'], pred_camera['crop_res']
cam_sxy, cam_rotmat, cam_intrinsics = pred_camera['cam_sxy'], pred_camera[
'cam_rotmat'], pred_camera['cam_intrinsics']
if 'cam_t' in pred_camera:
cam_t = pred_camera['cam_t']
else:
cam_t = convert_to_full_img_cam(
pare_cam=cam_sxy,
bbox_height=bbox_scale * 200.,
bbox_center=bbox_center,
img_w=img_w,
img_h=img_h,
focal_length=cam_intrinsics[:, 0, 0],
)
pred_keypoints_2d = perspective_projection(
pred_joints,
rotation=cam_rotmat,
translation=cam_t,
cam_intrinsics=cam_intrinsics,
)
return pred_keypoints_2d
def perspective_projection(
points,
rotation,
translation,
focal_length=None,
camera_center=None,
cam_intrinsics=None,
retain_z=False
):
"""
This function computes the perspective projection of a set of points.
Input:
points (bs, N, 3): 3D points
rotation (bs, 3, 3): Camera rotation
translation (bs, 3): Camera translation
focal_length (bs,) or scalar: Focal length
camera_center (bs, 2): Camera center
"""
batch_size = points.shape[0]
if cam_intrinsics is not None:
K = cam_intrinsics
else:
# raise
K = torch.zeros([batch_size, 3, 3], device=points.device)
K[:, 0, 0] = focal_length
K[:, 1, 1] = focal_length
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)
if retain_z:
return projected_points
else:
return projected_points[:, :, :-1]
def convert_to_full_img_cam(pare_cam, bbox_height, bbox_center, img_w, img_h, focal_length):
# Converts weak perspective camera estimated by PARE in
# bbox coords to perspective camera in full image coordinates
# from https://arxiv.org/pdf/2009.06549.pdf
s, tx, ty = pare_cam[:, 0], pare_cam[:, 1], pare_cam[:, 2]
res = 224
r = bbox_height / res
tz = 2 * focal_length / (r * res * s)
cx = 2 * (bbox_center[:, 0] - (img_w / 2.)) / (s * bbox_height)
cy = 2 * (bbox_center[:, 1] - (img_h / 2.)) / (s * bbox_height)
if torch.is_tensor(pare_cam):
cam_t = torch.stack([tx + cx, ty + cy, tz], dim=-1)
else:
cam_t = np.stack([tx + cx, ty + cy, tz], axis=-1)
return cam_t
def estimate_translation_np(S, joints_2d, joints_conf, focal_length=5000, img_size=(224., 224.)):
"""Find camera translation that brings 3D joints S closest to 2D the corresponding joints_2d.
Input:
S: (25, 3) 3D joint locations
joints: (25, 3) 2D joint locations and confidence
Returns:
(3,) camera translation vector
"""
num_joints = S.shape[0]
# focal length
f = np.array([focal_length, focal_length])
# optical center
center = np.array([img_size[1] / 2., img_size[0] / 2.])
# transformations
Z = np.reshape(np.tile(S[:, 2], (2, 1)).T, -1)
XY = np.reshape(S[:, 0:2], -1)
O = np.tile(center, num_joints)
F = np.tile(f, num_joints)
weight2 = np.reshape(np.tile(np.sqrt(joints_conf), (2, 1)).T, -1)
# least squares
Q = np.array([
F * np.tile(np.array([1, 0]), num_joints), F * np.tile(np.array([0, 1]), num_joints),
O - np.reshape(joints_2d, -1)
]).T
c = (np.reshape(joints_2d, -1) - O) * Z - F * XY
# weighted least squares
W = np.diagflat(weight2)
Q = np.dot(W, Q)
c = np.dot(W, c)
# square matrix
A = np.dot(Q.T, Q)
b = np.dot(Q.T, c)
# solution
trans = np.linalg.solve(A, b)
return trans
def estimate_translation(S, joints_2d, focal_length=5000., img_size=224., use_all_kps=False):
"""Find camera translation that brings 3D joints S closest to 2D the corresponding joints_2d.
Input:
S: (B, 49, 3) 3D joint locations
joints: (B, 49, 3) 2D joint locations and confidence
Returns:
(B, 3) camera translation vectors
"""
if isinstance(focal_length, numbers.Number):
focal_length = [
focal_length,
] * S.shape[0]
# print(len(focal_length), focal_length)
if isinstance(img_size, numbers.Number):
img_size = [
(img_size, img_size),
] * S.shape[0]
# print(len(img_size), img_size)
device = S.device
if use_all_kps:
S = S.cpu().numpy()
joints_2d = joints_2d.cpu().numpy()
else:
# Use only joints 25:49 (GT joints)
S = S[:, 25:, :].cpu().numpy()
joints_2d = joints_2d[:, 25:, :].cpu().numpy()
joints_conf = joints_2d[:, :, -1]
joints_2d = joints_2d[:, :, :-1]
trans = np.zeros((S.shape[0], 3), dtype=np.float32)
# Find the translation for each example in the batch
for i in range(S.shape[0]):
S_i = S[i]
joints_i = joints_2d[i]
conf_i = joints_conf[i]
trans[i] = estimate_translation_np(
S_i, joints_i, conf_i, focal_length=focal_length[i], img_size=img_size[i]
)
return torch.from_numpy(trans).to(device)
def Rot_y(angle, category='torch', prepend_dim=True, device=None):
'''Rotate around y-axis by angle
Args:
category: 'torch' or 'numpy'
prepend_dim: prepend an extra dimension
Return: Rotation matrix with shape [1, 3, 3] (prepend_dim=True)
'''
m = np.array([[np.cos(angle), 0., np.sin(angle)], [0., 1., 0.],
[-np.sin(angle), 0., np.cos(angle)]])
if category == 'torch':
if prepend_dim:
return torch.tensor(m, dtype=torch.float, device=device).unsqueeze(0)
else:
return torch.tensor(m, dtype=torch.float, device=device)
elif category == 'numpy':
if prepend_dim:
return np.expand_dims(m, 0)
else:
return m
else:
raise ValueError("category must be 'torch' or 'numpy'")
def Rot_x(angle, category='torch', prepend_dim=True, device=None):
'''Rotate around x-axis by angle
Args:
category: 'torch' or 'numpy'
prepend_dim: prepend an extra dimension
Return: Rotation matrix with shape [1, 3, 3] (prepend_dim=True)
'''
m = np.array([[1., 0., 0.], [0., np.cos(angle), -np.sin(angle)],
[0., np.sin(angle), np.cos(angle)]])
if category == 'torch':
if prepend_dim:
return torch.tensor(m, dtype=torch.float, device=device).unsqueeze(0)
else:
return torch.tensor(m, dtype=torch.float, device=device)
elif category == 'numpy':
if prepend_dim:
return np.expand_dims(m, 0)
else:
return m
else:
raise ValueError("category must be 'torch' or 'numpy'")
def Rot_z(angle, category='torch', prepend_dim=True, device=None):
'''Rotate around z-axis by angle
Args:
category: 'torch' or 'numpy'
prepend_dim: prepend an extra dimension
Return: Rotation matrix with shape [1, 3, 3] (prepend_dim=True)
'''
m = np.array([[np.cos(angle), -np.sin(angle), 0.], [np.sin(angle),
np.cos(angle), 0.], [0., 0., 1.]])
if category == 'torch':
if prepend_dim:
return torch.tensor(m, dtype=torch.float, device=device).unsqueeze(0)
else:
return torch.tensor(m, dtype=torch.float, device=device)
elif category == 'numpy':
if prepend_dim:
return np.expand_dims(m, 0)
else:
return m
else:
raise ValueError("category must be 'torch' or 'numpy'")
def compute_twist_rotation(rotation_matrix, twist_axis):
'''
Compute the twist component of given rotation and twist axis
https://stackoverflow.com/questions/3684269/component-of-a-quaternion-rotation-around-an-axis
Parameters
----------
rotation_matrix : Tensor (B, 3, 3,)
The rotation to convert
twist_axis : Tensor (B, 3,)
The twist axis
Returns
-------
Tensor (B, 3, 3)
The twist rotation
'''
quaternion = rotation_matrix_to_quaternion(rotation_matrix)
twist_axis = twist_axis / (torch.norm(twist_axis, dim=1, keepdim=True) + 1e-9)
projection = torch.einsum('bi,bi->b', twist_axis, quaternion[:, 1:]).unsqueeze(-1) * twist_axis
twist_quaternion = torch.cat([quaternion[:, 0:1], projection], dim=1)
twist_quaternion = twist_quaternion / (torch.norm(twist_quaternion, dim=1, keepdim=True) + 1e-9)
twist_rotation = quaternion_to_rotation_matrix(twist_quaternion)
twist_aa = quaternion_to_angle_axis(twist_quaternion)
twist_angle = torch.sum(twist_aa, dim=1,
keepdim=True) / torch.sum(twist_axis, dim=1, keepdim=True)
return twist_rotation, twist_angle