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import numpy as np
import torch
from smplx import MANO as _MANO
from smplx import MANOLayer as _MANOLayer
from detrsmpl.core.conventions.keypoints_mapping import (
convert_kps,
get_keypoint_num,
)
class MANO(_MANO):
"""Extension of the official MANO implementation."""
full_pose_keys = {'global_orient', 'hand_pose'}
NUM_VERTS = 776
NUM_FACES = 9976
KpId2manokps = {
0: 0, # Wrist
1: 5,
2: 6,
3: 7, # Index
4: 9,
5: 10,
6: 11, # Middle
7: 17,
8: 18,
9: 19, # Pinky
10: 13,
11: 14,
12: 15, # Ring
13: 1,
14: 2,
15: 3
} # Thumb
kpId2vertices = {
4: 744, # Thumb
8: 320, # Index
12: 443, # Middle
16: 555, # Ring
20: 672 # Pink
}
def __init__(self,
*args,
keypoint_src: str = 'mano',
keypoint_dst: str = 'human_data',
keypoint_approximate: bool = False,
**kwargs):
"""
Args:
*args: extra arguments for MANO initialization.
keypoint_src: source convention of keypoints. This convention
is used for keypoints obtained from joint regressors.
Keypoints then undergo conversion into keypoint_dst
convention.
keypoint_dst: destination convention of keypoints. This convention
is used for keypoints in the output.
keypoint_approximate: whether to use approximate matching in
convention conversion for keypoints.
**kwargs: extra keyword arguments for MANO initialization.
Returns:
None
"""
super(MANO, self).__init__(*args, **kwargs)
self.keypoint_src = keypoint_src
self.keypoint_dst = keypoint_dst
self.keypoint_approximate = keypoint_approximate
self.num_verts = self.get_num_verts()
self.num_faces = self.get_num_faces()
self.num_joints = get_keypoint_num(convention=self.keypoint_dst)
def forward(self,
*args,
return_verts: bool = True,
return_full_pose: bool = False,
**kwargs) -> dict:
"""Forward function.
Args:
*args: extra arguments for MANO
return_verts: whether to return vertices
return_full_pose: whether to return full pose parameters
**kwargs: extra arguments for MANO
Returns:
output: contains output parameters and attributes
"""
if 'right_hand_pose' in kwargs:
kwargs['hand_pose'] = kwargs['right_hand_pose']
mano_output = super(MANO, self).forward(*args, **kwargs)
joints = mano_output.joints
joints = self.get_keypoints_from_mesh(mano_output.vertices, joints)
joints, joint_mask = convert_kps(joints,
src=self.keypoint_src,
dst=self.keypoint_dst,
approximate=self.keypoint_approximate)
if isinstance(joint_mask, np.ndarray):
joint_mask = torch.tensor(joint_mask,
dtype=torch.uint8,
device=joints.device)
batch_size = joints.shape[0]
joint_mask = joint_mask.reshape(1, -1).expand(batch_size, -1)
output = dict(
global_orient=mano_output.global_orient,
hand_pose=mano_output.hand_pose,
joints=joints,
joint_mask=joint_mask,
keypoints=torch.cat([joints, joint_mask[:, :, None]], dim=-1),
betas=mano_output.betas,
)
if return_verts:
output['vertices'] = mano_output.vertices
if return_full_pose:
output['full_pose'] = mano_output.full_pose
return output
def get_keypoints_from_mesh(self, mesh_vertices, keypoints_regressed):
"""Assembles the full 21 keypoint set from the 16 Mano Keypoints and 5
mesh vertices for the fingers."""
batch_size = keypoints_regressed.shape[0]
keypoints = torch.zeros((batch_size, 21, 3)).cuda()
# fill keypoints which are regressed
for manoId, myId in self.KpId2manokps.items():
keypoints[:, myId, :] = keypoints_regressed[:, manoId, :]
# get other keypoints from mesh
for myId, meshId in self.kpId2vertices.items():
keypoints[:, myId, :] = mesh_vertices[:, meshId, :]
return keypoints
class MANOLayer(_MANOLayer):
"""Extension of the official MANO implementation."""
full_pose_keys = {'global_orient', 'hand_pose'}
NUM_VERTS = 776
NUM_FACES = 9976
KpId2manokps = {
0: 0, # Wrist
1: 5,
2: 6,
3: 7, # Index
4: 9,
5: 10,
6: 11, # Middle
7: 17,
8: 18,
9: 19, # Pinky
10: 13,
11: 14,
12: 15, # Ring
13: 1,
14: 2,
15: 3
} # Thumb
kpId2vertices = {
4: 744, # Thumb
8: 320, # Index
12: 443, # Middle
16: 555, # Ring
20: 672 # Pink
}
def __init__(self,
*args,
keypoint_src: str = 'mano',
keypoint_dst: str = 'human_data',
keypoint_approximate: bool = False,
**kwargs):
"""
Args:
*args: extra arguments for MANO initialization.
keypoint_src: source convention of keypoints. This convention
is used for keypoints obtained from joint regressors.
Keypoints then undergo conversion into keypoint_dst
convention.
keypoint_dst: destination convention of keypoints. This convention
is used for keypoints in the output.
keypoint_approximate: whether to use approximate matching in
convention conversion for keypoints.
**kwargs: extra keyword arguments for MANO initialization.
Returns:
None
"""
super(MANOLayer, self).__init__(*args, **kwargs)
self.keypoint_src = keypoint_src
self.keypoint_dst = keypoint_dst
self.keypoint_approximate = keypoint_approximate
self.num_verts = self.get_num_verts()
self.num_faces = self.get_num_faces()
self.num_joints = get_keypoint_num(convention=self.keypoint_dst)
def forward(self,
*args,
return_verts: bool = True,
return_full_pose: bool = False,
**kwargs) -> dict:
"""Forward function.
Args:
*args: extra arguments for MANO
return_verts: whether to return vertices
return_full_pose: whether to return full pose parameters
**kwargs: extra arguments for MANO
Returns:
output: contains output parameters and attributes
"""
if 'right_hand_pose' in kwargs:
kwargs['hand_pose'] = kwargs['right_hand_pose']
mano_output = super(MANOLayer, self).forward(*args, **kwargs)
joints = mano_output.joints
joints = self.get_keypoints_from_mesh(mano_output.vertices, joints)
joints, joint_mask = convert_kps(joints,
src=self.keypoint_src,
dst=self.keypoint_dst,
approximate=self.keypoint_approximate)
if isinstance(joint_mask, np.ndarray):
joint_mask = torch.tensor(joint_mask,
dtype=torch.uint8,
device=joints.device)
batch_size = joints.shape[0]
joint_mask = joint_mask.reshape(1, -1).expand(batch_size, -1)
output = dict(
global_orient=mano_output.global_orient,
hand_pose=mano_output.hand_pose,
joints=joints,
joint_mask=joint_mask,
keypoints=torch.cat([joints, joint_mask[:, :, None]], dim=-1),
betas=mano_output.betas,
)
if return_verts:
output['vertices'] = mano_output.vertices
if return_full_pose:
output['full_pose'] = mano_output.full_pose
return output
def get_keypoints_from_mesh(self, mesh_vertices, keypoints_regressed):
"""Assembles the full 21 keypoint set from the 16 Mano Keypoints and 5
mesh vertices for the fingers."""
batch_size = keypoints_regressed.shape[0]
keypoints = torch.zeros((batch_size, 21, 3)).cuda()
# fill keypoints which are regressed
for manoId, myId in self.KpId2manokps.items():
keypoints[:, myId, :] = keypoints_regressed[:, manoId, :]
# get other keypoints from mesh
for myId, meshId in self.kpId2vertices.items():
keypoints[:, myId, :] = mesh_vertices[:, meshId, :]
return keypoints
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