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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©2020 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: Vassilis Choutas, [email protected]
import os
import os.path as osp
import pickle
import numpy as np
import torch
from loguru import logger
from SMPLX.transfer_model.utils.typing import Tensor
def read_deformation_transfer(
deformation_transfer_path: str,
device=None,
use_normal: bool = False,
) -> Tensor:
''' Reads a deformation transfer
'''
if device is None:
device = torch.device('cpu')
assert osp.exists(deformation_transfer_path), (
'Deformation transfer path does not exist:'
f' {deformation_transfer_path}')
logger.info(
f'Loading deformation transfer from: {deformation_transfer_path}')
# Read the deformation transfer matrix
with open(deformation_transfer_path, 'rb') as f:
def_transfer_setup = pickle.load(f, encoding='latin1')
if 'mtx' in def_transfer_setup:
def_matrix = def_transfer_setup['mtx']
if hasattr(def_matrix, 'todense'):
def_matrix = def_matrix.todense()
def_matrix = np.array(def_matrix, dtype=np.float32)
if not use_normal:
num_verts = def_matrix.shape[1] // 2
def_matrix = def_matrix[:, :num_verts]
elif 'matrix' in def_transfer_setup:
def_matrix = def_transfer_setup['matrix']
else:
valid_keys = ['mtx', 'matrix']
raise KeyError(f'Deformation transfer setup must contain {valid_keys}')
def_matrix = torch.tensor(def_matrix, device=device, dtype=torch.float32)
return def_matrix
def apply_deformation_transfer(
def_matrix: Tensor,
vertices: Tensor,
faces: Tensor,
use_normals=False
) -> Tensor:
''' Applies the deformation transfer on the given meshes
'''
if use_normals:
raise NotImplementedError
else:
def_vertices = torch.einsum('mn,bni->bmi', [def_matrix, vertices])
return def_vertices
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