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# -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems and the Max Planck Institute for Biological
# Cybernetics. All rights reserved.
#
# Contact: ps-license@tuebingen.mpg.de
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os
import os.path as osp
import pickle
import numpy as np
from collections import namedtuple
import torch
import torch.nn as nn
from .lbs import (
lbs, vertices2joints, blend_shapes)
from .vertex_ids import vertex_ids as VERTEX_IDS
from .utils import Struct, to_np, to_tensor
from .vertex_joint_selector import VertexJointSelector
ModelOutput = namedtuple('ModelOutput',
['vertices','faces', 'joints', 'full_pose', 'betas',
'global_orient',
'body_pose', 'expression',
'left_hand_pose', 'right_hand_pose',
'jaw_pose', 'T', 'T_weighted', 'weights'])
ModelOutput.__new__.__defaults__ = (None,) * len(ModelOutput._fields)
class SMPL(nn.Module):
NUM_JOINTS = 23
NUM_BODY_JOINTS = 23
NUM_BETAS = 10
def __init__(self, model_path, data_struct=None,
create_betas=True,
betas=None,
create_global_orient=True,
global_orient=None,
create_body_pose=True,
body_pose=None,
create_transl=True,
transl=None,
dtype=torch.float32,
batch_size=1,
joint_mapper=None, gender='neutral',
vertex_ids=None,
pose_blend=True,
**kwargs):
''' SMPL model constructor
Parameters
----------
model_path: str
The path to the folder or to the file where the model
parameters are stored
data_struct: Strct
A struct object. If given, then the parameters of the model are
read from the object. Otherwise, the model tries to read the
parameters from the given `model_path`. (default = None)
create_global_orient: bool, optional
Flag for creating a member variable for the global orientation
of the body. (default = True)
global_orient: torch.tensor, optional, Bx3
The default value for the global orientation variable.
(default = None)
create_body_pose: bool, optional
Flag for creating a member variable for the pose of the body.
(default = True)
body_pose: torch.tensor, optional, Bx(Body Joints * 3)
The default value for the body pose variable.
(default = None)
create_betas: bool, optional
Flag for creating a member variable for the shape space
(default = True).
betas: torch.tensor, optional, Bx10
The default value for the shape member variable.
(default = None)
create_transl: bool, optional
Flag for creating a member variable for the translation
of the body. (default = True)
transl: torch.tensor, optional, Bx3
The default value for the transl variable.
(default = None)
dtype: torch.dtype, optional
The data type for the created variables
batch_size: int, optional
The batch size used for creating the member variables
joint_mapper: object, optional
An object that re-maps the joints. Useful if one wants to
re-order the SMPL joints to some other convention (e.g. MSCOCO)
(default = None)
gender: str, optional
Which gender to load
vertex_ids: dict, optional
A dictionary containing the indices of the extra vertices that
will be selected
'''
self.gender = gender
self.pose_blend = pose_blend
if data_struct is None:
if osp.isdir(model_path):
model_fn = 'SMPL_{}.{ext}'.format(gender.upper(), ext='pkl')
smpl_path = os.path.join(model_path, model_fn)
else:
smpl_path = model_path
assert osp.exists(smpl_path), 'Path {} does not exist!'.format(
smpl_path)
with open(smpl_path, 'rb') as smpl_file:
data_struct = Struct(**pickle.load(smpl_file,encoding='latin1'))
super(SMPL, self).__init__()
self.batch_size = batch_size
if vertex_ids is None:
# SMPL and SMPL-H share the same topology, so any extra joints can
# be drawn from the same place
vertex_ids = VERTEX_IDS['smplh']
self.dtype = dtype
self.joint_mapper = joint_mapper
self.vertex_joint_selector = VertexJointSelector(
vertex_ids=vertex_ids, **kwargs)
self.faces = data_struct.f
self.register_buffer('faces_tensor',
to_tensor(to_np(self.faces, dtype=np.int64),
dtype=torch.long))
if create_betas:
if betas is None:
default_betas = torch.zeros([batch_size, self.NUM_BETAS],
dtype=dtype)
else:
if 'torch.Tensor' in str(type(betas)):
default_betas = betas.clone().detach()
else:
default_betas = torch.tensor(betas,
dtype=dtype)
self.register_parameter('betas', nn.Parameter(default_betas,
requires_grad=True))
# The tensor that contains the global rotation of the model
# It is separated from the pose of the joints in case we wish to
# optimize only over one of them
if create_global_orient:
if global_orient is None:
default_global_orient = torch.zeros([batch_size, 3],
dtype=dtype)
else:
if 'torch.Tensor' in str(type(global_orient)):
default_global_orient = global_orient.clone().detach()
else:
default_global_orient = torch.tensor(global_orient,
dtype=dtype)
global_orient = nn.Parameter(default_global_orient,
requires_grad=True)
self.register_parameter('global_orient', global_orient)
if create_body_pose:
if body_pose is None:
default_body_pose = torch.zeros(
[batch_size, self.NUM_BODY_JOINTS * 3], dtype=dtype)
else:
if 'torch.Tensor' in str(type(body_pose)):
default_body_pose = body_pose.clone().detach()
else:
default_body_pose = torch.tensor(body_pose,
dtype=dtype)
self.register_parameter(
'body_pose',
nn.Parameter(default_body_pose, requires_grad=True))
if create_transl:
if transl is None:
default_transl = torch.zeros([batch_size, 3],
dtype=dtype,
requires_grad=True)
else:
default_transl = torch.tensor(transl, dtype=dtype)
self.register_parameter(
'transl',
nn.Parameter(default_transl, requires_grad=True))
# The vertices of the template model
self.register_buffer('v_template',
to_tensor(to_np(data_struct.v_template),
dtype=dtype))
# The shape components
shapedirs = data_struct.shapedirs[:, :, :self.NUM_BETAS]
# The shape components
self.register_buffer(
'shapedirs',
to_tensor(to_np(shapedirs), dtype=dtype))
j_regressor = to_tensor(to_np(
data_struct.J_regressor), dtype=dtype)
self.register_buffer('J_regressor', j_regressor)
# if self.gender == 'neutral':
# joint_regressor = to_tensor(to_np(
# data_struct.cocoplus_regressor), dtype=dtype).permute(1,0)
# self.register_buffer('joint_regressor', joint_regressor)
# Pose blend shape basis: 6890 x 3 x 207, reshaped to 6890*3 x 207
num_pose_basis = data_struct.posedirs.shape[-1]
# 207 x 20670
posedirs = np.reshape(data_struct.posedirs, [-1, num_pose_basis]).T
self.register_buffer('posedirs',
to_tensor(to_np(posedirs), dtype=dtype))
# indices of parents for each joints
parents = to_tensor(to_np(data_struct.kintree_table[0])).long()
parents[0] = -1
self.register_buffer('parents', parents)
self.bone_parents = to_np(data_struct.kintree_table[0])
self.register_buffer('lbs_weights',
to_tensor(to_np(data_struct.weights), dtype=dtype))
def create_mean_pose(self, data_struct):
pass
@torch.no_grad()
def reset_params(self, **params_dict):
for param_name, param in self.named_parameters():
if param_name in params_dict:
param[:] = torch.tensor(params_dict[param_name])
else:
param.fill_(0)
def get_T_hip(self, betas=None):
v_shaped = self.v_template + blend_shapes(betas, self.shapedirs)
J = vertices2joints(self.J_regressor, v_shaped)
T_hip = J[0,0]
return T_hip
def get_num_verts(self):
return self.v_template.shape[0]
def get_num_faces(self):
return self.faces.shape[0]
def extra_repr(self):
return 'Number of betas: {}'.format(self.NUM_BETAS)
def forward(self, betas=None, body_pose=None, global_orient=None,
transl=None, return_verts=True, return_full_pose=False,displacement=None,v_template=None,
**kwargs):
''' Forward pass for the SMPL model
Parameters
----------
global_orient: torch.tensor, optional, shape Bx3
If given, ignore the member variable and use it as the global
rotation of the body. Useful if someone wishes to predicts this
with an external model. (default=None)
betas: torch.tensor, optional, shape Bx10
If given, ignore the member variable `betas` and use it
instead. For example, it can used if shape parameters
`betas` are predicted from some external model.
(default=None)
body_pose: torch.tensor, optional, shape Bx(J*3)
If given, ignore the member variable `body_pose` and use it
instead. For example, it can used if someone predicts the
pose of the body joints are predicted from some external model.
It should be a tensor that contains joint rotations in
axis-angle format. (default=None)
transl: torch.tensor, optional, shape Bx3
If given, ignore the member variable `transl` and use it
instead. For example, it can used if the translation
`transl` is predicted from some external model.
(default=None)
return_verts: bool, optional
Return the vertices. (default=True)
return_full_pose: bool, optional
Returns the full axis-angle pose vector (default=False)
Returns
-------
'''
# If no shape and pose parameters are passed along, then use the
# ones from the module
global_orient = (global_orient if global_orient is not None else
self.global_orient)
body_pose = body_pose if body_pose is not None else self.body_pose
betas = betas if betas is not None else self.betas
apply_trans = transl is not None or hasattr(self, 'transl')
if transl is None and hasattr(self, 'transl'):
transl = self.transl
full_pose = torch.cat([global_orient, body_pose], dim=1)
# if betas.shape[0] != self.batch_size:
# num_repeats = int(self.batch_size / betas.shape[0])
# betas = betas.expand(num_repeats, -1)
if v_template is None:
v_template = self.v_template
if displacement is not None:
vertices, joints_smpl, T_weighted, W, T = lbs(betas, full_pose, v_template+displacement,
self.shapedirs, self.posedirs,
self.J_regressor, self.parents,
self.lbs_weights, dtype=self.dtype,pose_blend=self.pose_blend)
else:
vertices, joints_smpl,T_weighted, W, T = lbs(betas, full_pose, v_template,
self.shapedirs, self.posedirs,
self.J_regressor, self.parents,
self.lbs_weights, dtype=self.dtype,pose_blend=self.pose_blend)
# if self.gender is not 'neutral':
joints = self.vertex_joint_selector(vertices, joints_smpl)
# else:
# joints = torch.matmul(vertices.permute(0,2,1),self.joint_regressor).permute(0,2,1)
# Map the joints to the current dataset
if self.joint_mapper is not None:
joints = self.joint_mapper(joints)
if apply_trans:
joints_smpl = joints_smpl + transl.unsqueeze(dim=1)
joints = joints + transl.unsqueeze(dim=1)
vertices = vertices + transl.unsqueeze(dim=1)
output = ModelOutput(vertices=vertices if return_verts else None,
faces=self.faces,
global_orient=global_orient,
body_pose=body_pose,
joints=joints_smpl,
betas=self.betas,
full_pose=full_pose if return_full_pose else None,
T=T, T_weighted=T_weighted, weights=W)
return output |