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from typing import Tuple
import numpy as np
import utils.constants as constants
import torch
class HybrIKJointsToRotmat:
def __init__(self):
self.naive_hybrik = constants.SMPL_HYBRIK
self.num_nodes = 22
self.parents = constants.SMPL_BODY_PARENTS
self.child = constants.SMPL_BODY_CHILDS
self.bones = np.array(constants.SMPL_BODY_BONES).reshape(24, 3)[
: self.num_nodes
]
def multi_child_rot(
self, t: np.ndarray, p: np.ndarray, pose_global_parent: np.ndarray
) -> Tuple[np.ndarray]:
"""
t: B x 3 x child_num
p: B x 3 x child_num
pose_global_parent: B x 3 x 3
"""
m = np.matmul(
t, np.transpose(np.matmul(np.linalg.inv(pose_global_parent), p), [0, 2, 1])
)
u, s, vt = np.linalg.svd(m)
r = np.matmul(np.transpose(vt, [0, 2, 1]), np.transpose(u, [0, 2, 1]))
err_det_mask = (np.linalg.det(r) < 0.0).reshape(-1, 1, 1)
id_fix = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, -1.0]]).reshape(
1, 3, 3
)
r_fix = np.matmul(
np.transpose(vt, [0, 2, 1]), np.matmul(id_fix, np.transpose(u, [0, 2, 1]))
)
r = r * (1.0 - err_det_mask) + r_fix * err_det_mask
return r, np.matmul(pose_global_parent, r)
def single_child_rot(
self,
t: np.ndarray,
p: np.ndarray,
pose_global_parent: np.ndarray,
twist: np.ndarray = None,
) -> Tuple[np.ndarray]:
"""
t: B x 3 x 1
p: B x 3 x 1
pose_global_parent: B x 3 x 3
twist: B x 2 if given, default to None
"""
p_rot = np.matmul(np.linalg.inv(pose_global_parent), p)
cross = np.cross(t, p_rot, axisa=1, axisb=1, axisc=1)
sina = np.linalg.norm(cross, axis=1, keepdims=True) / (
np.linalg.norm(t, axis=1, keepdims=True)
* np.linalg.norm(p_rot, axis=1, keepdims=True)
)
cross = cross / np.linalg.norm(cross, axis=1, keepdims=True)
cosa = np.sum(t * p_rot, axis=1, keepdims=True) / (
np.linalg.norm(t, axis=1, keepdims=True)
* np.linalg.norm(p_rot, axis=1, keepdims=True)
)
sina = sina.reshape(-1, 1, 1)
cosa = cosa.reshape(-1, 1, 1)
skew_sym_t = np.stack(
[
0.0 * cross[:, 0],
-cross[:, 2],
cross[:, 1],
cross[:, 2],
0.0 * cross[:, 0],
-cross[:, 0],
-cross[:, 1],
cross[:, 0],
0.0 * cross[:, 0],
],
1,
)
skew_sym_t = skew_sym_t.reshape(-1, 3, 3)
dsw_rotmat = (
np.eye(3).reshape(1, 3, 3)
+ sina * skew_sym_t
+ (1.0 - cosa) * np.matmul(skew_sym_t, skew_sym_t)
)
if twist is not None:
skew_sym_t = np.stack(
[
0.0 * t[:, 0],
-t[:, 2],
t[:, 1],
t[:, 2],
0.0 * t[:, 0],
-t[:, 0],
-t[:, 1],
t[:, 0],
0.0 * t[:, 0],
],
1,
)
skew_sym_t = skew_sym_t.reshape(-1, 3, 3)
sina = twist[:, 1].reshape(-1, 1, 1)
cosa = twist[:, 0].reshape(-1, 1, 1)
dtw_rotmat = (
np.eye(3).reshape([1, 3, 3])
+ sina * skew_sym_t
+ (1.0 - cosa) * np.matmul(skew_sym_t, skew_sym_t)
)
dsw_rotmat = np.matmul(dsw_rotmat, dtw_rotmat)
return dsw_rotmat, np.matmul(pose_global_parent, dsw_rotmat)
def __call__(self, joints: np.ndarray, twist: np.ndarray = None) -> np.ndarray:
"""
joints: B x N x 3
twist: B x N x 2 if given, default to None
"""
expand_dim = False
if len(joints.shape) == 2:
expand_dim = True
joints = np.expand_dims(joints, 0)
if twist is not None:
twist = np.expand_dims(twist, 0)
assert len(joints.shape) == 3
batch_size = np.shape(joints)[0]
joints_rel = joints - joints[:, self.parents]
joints_hybrik = 0.0 * joints_rel
pose_global = np.zeros([batch_size, self.num_nodes, 3, 3])
pose = np.zeros([batch_size, self.num_nodes, 3, 3])
for i in range(self.num_nodes):
if i == 0:
joints_hybrik[:, 0] = joints[:, 0]
else:
joints_hybrik[:, i] = (
np.matmul(
pose_global[:, self.parents[i]],
self.bones[i].reshape(1, 3, 1),
).reshape(-1, 3)
+ joints_hybrik[:, self.parents[i]]
)
if self.child[i] == -2:
pose[:, i] = pose[:, i] + np.eye(3).reshape(1, 3, 3)
pose_global[:, i] = pose_global[:, self.parents[i]]
continue
if i == 0:
r, rg = self.multi_child_rot(
np.transpose(self.bones[[1, 2, 3]].reshape(1, 3, 3), [0, 2, 1]),
np.transpose(joints_rel[:, [1, 2, 3]], [0, 2, 1]),
np.eye(3).reshape(1, 3, 3),
)
elif i == 9:
r, rg = self.multi_child_rot(
np.transpose(self.bones[[12, 13, 14]].reshape(1, 3, 3), [0, 2, 1]),
np.transpose(joints_rel[:, [12, 13, 14]], [0, 2, 1]),
pose_global[:, self.parents[9]],
)
else:
p = joints_rel[:, self.child[i]]
if self.naive_hybrik[i] == 0:
p = joints[:, self.child[i]] - joints_hybrik[:, i]
twi = None
if twist is not None:
twi = twist[:, i]
r, rg = self.single_child_rot(
self.bones[self.child[i]].reshape(1, 3, 1),
p.reshape(-1, 3, 1),
pose_global[:, self.parents[i]],
twi,
)
pose[:, i] = r
pose_global[:, i] = rg
if expand_dim:
pose = pose[0]
return pose
class HybrIKJointsToRotmat_Tensor:
def __init__(self):
self.naive_hybrik = constants.SMPL_HYBRIK
self.num_nodes = 22
self.parents = constants.SMPL_BODY_PARENTS
self.child = constants.SMPL_BODY_CHILDS
self.bones = torch.tensor(constants.SMPL_BODY_BONES).reshape(24, 3)[:self.num_nodes]
def multi_child_rot(self, t, p, pose_global_parent):
"""
t: B x 3 x child_num
p: B x 3 x child_num
pose_global_parent: B x 3 x 3
"""
m = torch.matmul(
t, torch.transpose(torch.matmul(torch.inverse(pose_global_parent), p), 1, 2)
)
u, s, vt = torch.linalg.svd(m)
r = torch.matmul(torch.transpose(vt, 1, 2), torch.transpose(u, 1, 2))
err_det_mask = (torch.det(r) < 0.0).reshape(-1, 1, 1)
id_fix = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, -1.0]]).reshape(1, 3, 3)
r_fix = torch.matmul(
torch.transpose(vt, 1, 2), torch.matmul(id_fix, torch.transpose(u, 1, 2))
)
r = r * (~err_det_mask) + r_fix * err_det_mask
return r, torch.matmul(pose_global_parent, r)
def single_child_rot(
self,
t,
p,
pose_global_parent,
twist = None,
) -> Tuple[torch.Tensor]:
"""
t: B x 3 x 1
p: B x 3 x 1
pose_global_parent: B x 3 x 3
twist: B x 2 if given, default to None
"""
t_tensor = t.clone().detach()#torch.tensor(t)
p_tensor = p.clone().detach()#torch.tensor(p)
pose_global_parent_tensor = pose_global_parent.clone().detach()#torch.tensor(pose_global_parent)
p_rot = torch.matmul(torch.linalg.inv(pose_global_parent_tensor), p_tensor)
cross = torch.cross(t_tensor, p_rot, dim=1)
sina = torch.linalg.norm(cross, dim=1, keepdim=True) / (
torch.linalg.norm(t_tensor, dim=1, keepdim=True)
* torch.linalg.norm(p_rot, dim=1, keepdim=True)
)
cross = cross / torch.linalg.norm(cross, dim=1, keepdim=True)
cosa = torch.sum(t_tensor * p_rot, dim=1, keepdim=True) / (
torch.linalg.norm(t_tensor, dim=1, keepdim=True)
* torch.linalg.norm(p_rot, dim=1, keepdim=True)
)
sina = sina.reshape(-1, 1, 1)
cosa = cosa.reshape(-1, 1, 1)
skew_sym_t = torch.stack(
[
0.0 * cross[:, 0],
-cross[:, 2],
cross[:, 1],
cross[:, 2],
0.0 * cross[:, 0],
-cross[:, 0],
-cross[:, 1],
cross[:, 0],
0.0 * cross[:, 0],
],
1,
)
skew_sym_t = skew_sym_t.reshape(-1, 3, 3)
dsw_rotmat = (
torch.eye(3).reshape(1, 3, 3)
+ sina * skew_sym_t
+ (1.0 - cosa) * torch.matmul(skew_sym_t, skew_sym_t)
)
if twist is not None:
twist_tensor = torch.tensor(twist)
skew_sym_t = torch.stack(
[
0.0 * t_tensor[:, 0],
-t_tensor[:, 2],
t_tensor[:, 1],
t_tensor[:, 2],
0.0 * t_tensor[:, 0],
-t_tensor[:, 0],
-t_tensor[:, 1],
t_tensor[:, 0],
0.0 * t_tensor[:, 0],
],
1,
)
skew_sym_t = skew_sym_t.reshape(-1, 3, 3)
sina = twist_tensor[:, 1].reshape(-1, 1, 1)
cosa = twist_tensor[:, 0].reshape(-1, 1, 1)
dtw_rotmat = (
torch.eye(3).reshape([1, 3, 3])
+ sina * skew_sym_t
+ (1.0 - cosa) * torch.matmul(skew_sym_t, skew_sym_t)
)
dsw_rotmat = torch.matmul(dsw_rotmat, dtw_rotmat)
return dsw_rotmat, torch.matmul(pose_global_parent_tensor, dsw_rotmat)
def __call__(self, joints, twist = None) -> torch.Tensor:
"""
joints: B x N x 3
twist: B x N x 2 if given, default to None
"""
expand_dim = False
if len(joints.shape) == 2:
expand_dim = True
joints = joints.unsqueeze(0)
if twist is not None:
twist = twist.unsqueeze(0)
assert len(joints.shape) == 3
batch_size = joints.shape[0]
joints_rel = joints - joints[:, self.parents]
joints_hybrik = torch.zeros_like(joints_rel)
pose_global = torch.zeros([batch_size, self.num_nodes, 3, 3])
pose = torch.zeros([batch_size, self.num_nodes, 3, 3])
for i in range(self.num_nodes):
if i == 0:
joints_hybrik[:, 0] = joints[:, 0]
else:
joints_hybrik[:, i] = (
torch.matmul(
pose_global[:, self.parents[i]],
self.bones[i].reshape(1, 3, 1),
).reshape(-1, 3)
+ joints_hybrik[:, self.parents[i]]
)
if self.child[i] == -2:
pose[:, i] = pose[:, i] + torch.eye(3).reshape(1, 3, 3)
pose_global[:, i] = pose_global[:, self.parents[i]]
continue
if i == 0:
t = self.bones[[1, 2, 3]].reshape(1, 3, 3).permute(0, 2, 1)
p = joints_rel[:, [1, 2, 3]].permute(0, 2, 1)
pose_global_parent = torch.eye(3).reshape(1, 3, 3)
r, rg = self.multi_child_rot(t, p, pose_global_parent)
elif i == 9:
t = self.bones[[12, 13, 14]].reshape(1, 3, 3).permute(0, 2, 1)
p = joints_rel[:, [12, 13, 14]].permute(0, 2, 1)
r, rg = self.multi_child_rot(t, p, pose_global[:, self.parents[9]],)
else:
p = joints_rel[:, self.child[i]]
if self.naive_hybrik[i] == 0:
p = joints[:, self.child[i]] - joints_hybrik[:, i]
twi = None
if twist is not None:
twi = twist[:, i]
t = self.bones[self.child[i]].reshape(-1, 3, 1)
p = p.reshape(-1, 3, 1)
nframes, _, _ = p.shape
t = t.repeat(nframes, 1, 1)
r, rg = self.single_child_rot(t, p, pose_global[:, self.parents[i]], twi)
pose[:, i] = r
pose_global[:, i] = rg
if expand_dim:
pose = pose[0]
return pose
if __name__ == "__main__":
jts2rot_hybrik = HybrIKJointsToRotmat_Tensor()
joints = torch.tensor(constants.SMPL_BODY_BONES).reshape(1, 24, 3)[:, :22]
parents = [0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 9, 12, 13, 14, 16, 17, 18, 19]
for i in range(1, 22):
joints[:, i] = joints[:, i] + joints[:, parents[i]]
print(joints.shape)
pose = jts2rot_hybrik(joints)
print(pose.shape)
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