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fittingop.py
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fittingop.py
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import os
import sys
sys.path.append(os.getcwd())
import copy
import json
import pickle
import time
import numpy as np
import open3d as o3d
import smplx
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from human_body_prior.tools.model_loader import load_vposer
from torch.autograd import Variable
from utils.train_helper import EarlyStopping, point2point_signed
from utils.utils import RotConverter
class FittingOP:
def __init__(self, fittingconfig):
for key, val in fittingconfig.items():
setattr(self, key, val)
body_model_path = './body_utils/body_models'
self.bm_male = smplx.create(body_model_path, model_type='smplx',
gender='male', ext='npz',
num_pca_comps=self.hand_ncomps,
create_global_orient=True,
create_body_pose=True,
create_betas=True,
create_left_hand_pose=True,
create_right_hand_pose=True,
create_expression=True,
create_jaw_pose=True,
create_leye_pose=True,
create_reye_pose=True,
create_transl=True,
batch_size=self.batch_size
)
self.bm_female = smplx.create(body_model_path, model_type='smplx',
gender='female', ext='npz',
num_pca_comps=self.hand_ncomps,
create_global_orient=True,
create_body_pose=True,
create_betas=True,
create_left_hand_pose=True,
create_right_hand_pose=True,
create_expression=True,
create_jaw_pose=True,
create_leye_pose=True,
create_reye_pose=True,
create_transl=True,
batch_size=self.batch_size
)
self.bm_male.to(self.device)
self.bm_female.to(self.device)
self.bm_male.eval()
self.bm_female.eval()
self.bm = None
self.vposer, _ = load_vposer(body_model_path+'/vposer_v1_0', vp_model='snapshot')
self.vposer.to(self.device)
self.vposer.eval()
self.fittingconfig = fittingconfig
## setup optim variables
self.betas = Variable(torch.zeros(self.batch_size,10).to(self.device), requires_grad=True)
self.transl_rec = Variable(torch.zeros(self.batch_size,3).to(self.device), requires_grad=True)
self.glo_rot_rec = Variable(torch.FloatTensor([[-1,0,0,0,0,1]]).repeat(self.batch_size,1).to(self.device), requires_grad=True)
self.vpose_rec = Variable(torch.zeros(self.batch_size,32).to(self.device), requires_grad=True)
self.hand_pose = Variable(torch.zeros(self.batch_size,2*self.hand_ncomps).to(self.device), requires_grad=True)
self.eye_pose = Variable(torch.zeros(self.batch_size,6).to(self.device), requires_grad=True)
self.optimizer_s1 = optim.Adam([self.transl_rec, self.glo_rot_rec], lr=self.init_lr_h*2.0)
self.optimizer_s2 = optim.Adam([self.betas, self.transl_rec, self.glo_rot_rec, self.vpose_rec], lr=self.init_lr_h*1.5)
self.optimizer_s3 = optim.Adam([self.vpose_rec, self.hand_pose, self.eye_pose],
lr=self.init_lr_h)
self.optimizers = [self.optimizer_s1,
self.optimizer_s2,
self.optimizer_s3]
self.v_weights = torch.from_numpy(np.load(self.cfg.c_weights_path)).to(torch.float32).to(self.device)
self.v_weights2 = torch.pow(self.v_weights, 1.0 / 2.5)
with open('./body_utils/smplx_markerset.json') as f:
markerset = json.load(f)['markersets']
self.markers_143 = []
for marker in markerset:
if marker['type'] not in ['palm_5']:
self.markers_143 += list(marker['indices'].values())
mano_fname = './body_utils/smplx_mano_flame_correspondences/MANO_SMPLX_vertex_ids.pkl'
with open(mano_fname, 'rb') as f:
idxs_data = pickle.load(f)
self.rhand_verts = idxs_data['right_hand']
self.lhand_verts = idxs_data['left_hand']
body_segments_dir = './body_utils/body_segments'
with open(os.path.join(body_segments_dir, 'L_Leg.json'), 'r') as f:
data = json.load(f)
left_foot_verts_id = np.asarray(list(set(data["verts_ind"])))
left_heel_verts_id = np.load(
'./body_utils/left_heel_verts_id.npy')
left_toe_verts_id = np.load(
'./body_utils/left_toe_verts_id.npy')
self.left_heel_verts_id = left_foot_verts_id[left_heel_verts_id]
self.left_toe_verts_id = left_foot_verts_id[left_toe_verts_id]
with open(os.path.join(body_segments_dir, 'R_Leg.json'), 'r') as f:
data = json.load(f)
right_foot_verts_id = np.asarray(list(set(data["verts_ind"])))
right_heel_verts_id = np.load(
'./body_utils/right_heel_verts_id.npy')
right_toe_verts_id = np.load(
'./body_utils/right_toe_verts_id.npy')
self.right_heel_verts_id = right_foot_verts_id[right_heel_verts_id]
self.right_toe_verts_id = right_foot_verts_id[right_toe_verts_id]
self.foot_markers_all = np.concatenate([self.right_heel_verts_id, self.right_toe_verts_id, self.left_heel_verts_id, self.left_toe_verts_id], axis=0)
def init_betas(self, betas):
self.betas.data = torch.nn.Parameter(torch.FloatTensor(betas).to(self.device).repeat(self.batch_size, 1))
def reset(self):
self.betas.data = torch.nn.Parameter(torch.zeros(self.batch_size,10).to(self.device))
self.transl_rec.data = torch.nn.Parameter(torch.zeros(self.batch_size,3).to(self.device))
self.glo_rot_rec.data = torch.nn.Parameter(torch.FloatTensor([[-1,0,0,0,0,1]]).to(self.device).repeat(self.batch_size,1))
self.vpose_rec.data = torch.nn.Parameter(torch.zeros(self.batch_size,32).to(self.device))
self.hand_pose.data = torch.nn.Parameter(torch.zeros(self.batch_size,2*self.hand_ncomps).to(self.device))
self.eye_pose.data = torch.nn.Parameter(torch.zeros(self.batch_size,6).to(self.device))
def calc_loss_contact_map(self, body_markers, verts_object, normal_object, contacts_object, contacts_markers, gender, betas, stage, alpha):
body_param = {}
body_param['transl'] = self.transl_rec
body_param['global_orient'] = RotConverter.rotmat2aa(RotConverter.cont2rotmat(self.glo_rot_rec))
body_param['betas'] = self.betas
body_param['body_pose'] = self.vposer.decode(self.vpose_rec,
output_type='aa').view(self.batch_size, -1)
body_param['left_hand_pose'] = self.hand_pose[:,:self.hand_ncomps]
body_param['right_hand_pose'] = self.hand_pose[:,self.hand_ncomps:]
body_param['leye_pose'] = self.eye_pose[:,:3]
body_param['reye_pose'] = self.eye_pose[:,3:]
output = self.bm(return_verts=True, **body_param)
verts_full = output.vertices
joints = output.joints
body_markers_rec = verts_full[:, self.markers_143, :]
foot_markers_rec = verts_full[:,self.foot_markers_all,:]
rhand_verts_rec = verts_full[:, self.rhand_verts, :]
############################
# compute normal
mesh = o3d.geometry.TriangleMesh()
verts_full_new = verts_full.detach().cpu().numpy()[0]
mesh.vertices = o3d.utility.Vector3dVector(verts_full_new)
mesh.triangles = o3d.utility.Vector3iVector(self.faces)
mesh.compute_vertex_normals()
normals = np.asarray(mesh.vertex_normals)
rh_normals = torch.tensor(normals[self.rhand_verts, :]).to(torch.float32).to(self.device).view(-1, 778, 3)
##################################################
##################################################
# markers reconstruction loss
loss_rec = torch.mean(torch.abs(body_markers_rec-body_markers.detach()))
loss_body_rec = torch.mean(torch.abs(body_markers_rec[:, :49, :]-body_markers.detach()[:, :49, :]))
#################################################
# foot loss
loss_foot = 0.1 * torch.mean(torch.abs(foot_markers_rec[:,:,-1]))
#################################################
# regularization
loss_vpose_reg = 0.0001*torch.mean(self.vpose_rec**2)
loss_hand_pose_reg = 0.0005*torch.mean(self.hand_pose**2)
loss_eye_pose_reg = 0.0001*torch.mean(self.eye_pose**2)
#################################################
# contact map loss
o2h_marker, h2o_signed_marker, o2h_idx_marker, _ = point2point_signed(body_markers_rec, verts_object.float(), normal_object.float())
o2h_signed, h2o_signed, o2h_idx, _ = point2point_signed(rhand_verts_rec, verts_object.float(), rh_normals, normal_object.float())
# map_weight = torch.gather(contacts_markers.view(1, -1), 1, o2h_idx_marker.long())
# contacts_object_pred = (1 - 2 * (torch.sigmoid(o2h_marker*150)-0.5)) * map_weight
# loss_contact_map = torch.mean((contacts_object_pred-contacts_object)**2) # not used
loss_marker_contact = torch.mean(torch.abs(h2o_signed_marker)*contacts_markers.view(1, -1))
loss_object_contact = torch.mean(torch.abs(o2h_signed)*contacts_object.view(1, -1))
##########################################################
v_contact = torch.zeros([1, h2o_signed.size(1)]).to(self.device)
v_collision = torch.zeros([1, h2o_signed.size(1)]).to(self.device)
v_dist = (h2o_signed < 0.02) * (h2o_signed > 0) * (self.v_weights2[None] > 0.7)
v_dist_neg = h2o_signed < 0
v_dist_marker_neg = h2o_signed_marker < 0
v_contact[v_dist] = 1 * self.v_weights[None][v_dist] # weight for close vertices
v_collision[v_dist_neg] = 10 # large weight for penetration
w = torch.zeros([1, o2h_signed.size(1)]).to(self.device)
w_dist = (o2h_signed < 0.01) * (o2h_signed > 0)
w_dist_neg = o2h_signed < 0
w[w_dist] = 0 # small weight for far away vertices
w[w_dist_neg] = 20 # large weight for penetration
f = torch.nn.ReLU()
loss_prior_contact = 1 * torch.mean(torch.einsum('ij,ij->ij', torch.abs(h2o_signed), v_contact)) # replace with key markers
h_collision = 1 * torch.mean(torch.einsum('ij,ij->ij', torch.abs(h2o_signed), v_collision)) # keep it
loss_dist_o = 1 * torch.mean(torch.einsum('ij,ij->ij', torch.abs(o2h_signed), w)) #
##################################################
loss = (1 * loss_rec
+ (self.only_rec==False) * (self.contact_loss=='contact') * (stage>1) * 15 * (loss_marker_contact+loss_object_contact) # 2 / 0 / 0
+ (self.only_rec==False) * (self.contact_loss=='prior') * (stage>1) * 15 * loss_prior_contact # 2 / 0 / 0
+ (self.only_rec==False) * (stage>1)* 10 * (h_collision+loss_dist_o)
+ (stage>1)*loss_hand_pose_reg
+ (stage>1)*loss_eye_pose_reg
+ (stage>0)*loss_vpose_reg
+ 0.1*loss_foot
)
loss_dict = {}
loss_dict['total'] = loss.detach().cpu().numpy()
loss_dict['rec'] = loss_rec.detach().cpu().numpy()
loss_dict['body_rec'] = loss_body_rec.detach().cpu().numpy()
# loss_dict['contact map diff'] = loss_contact_map.detach().cpu().numpy()
loss_dict['marker contact'] = loss_marker_contact.detach().cpu().numpy()
loss_dict['object contact'] = loss_object_contact.detach().cpu().numpy()
loss_dict['prior contact'] = loss_prior_contact.detach().cpu().numpy()
loss_dict['hand collision'] = h_collision.detach().cpu().numpy()
loss_dict['object collision'] = loss_dist_o.detach().cpu().numpy()
loss_dict['foot'] = loss_foot.detach().cpu().numpy()
loss_dict['reg'] = (loss_vpose_reg+loss_hand_pose_reg+loss_eye_pose_reg).detach().cpu().numpy()
vertices_info = {}
vertices_info['hand colli'] = torch.where(v_dist_neg==True)[0].size()[0]
vertices_info['obj colli'] = torch.where(w_dist_neg==True)[0].size()[0]
vertices_info['contact'] = torch.where((h2o_signed < 0.001) * (h2o_signed > -0.001)==True)[0].size()[0]
vertices_info['hand markers colli'] = torch.where(v_dist_marker_neg==True)[0].size()[0]
return loss, loss_dict, body_markers_rec, body_param, vertices_info, rhand_verts_rec, rh_normals, h2o_signed, o2h_signed
def calc_loss(self, body_markers, verts_object, normal_object, gender, betas, stage, alpha):
body_param = {}
body_param['transl'] = self.transl_rec
body_param['global_orient'] = RotConverter.rotmat2aa(RotConverter.cont2rotmat(self.glo_rot_rec))
body_param['betas'] = self.betas
# body_param['betas'] = torch.tensor(betas, dtype=torch.float32, requires_grad=False).repeat(self.batch_size, 1).to(self.device)
body_param['body_pose'] = self.vposer.decode(self.vpose_rec,
output_type='aa').view(self.batch_size, -1)
body_param['left_hand_pose'] = self.hand_pose[:,:self.hand_ncomps] #+ self.delta_hand_pose[:,:self.hand_ncomps]
body_param['right_hand_pose'] = self.hand_pose[:,self.hand_ncomps:] #+ self.delta_hand_pose[:,self.hand_ncomps:]
body_param['leye_pose'] = self.eye_pose[:,:3]
body_param['reye_pose'] = self.eye_pose[:,3:]
output = self.bm(return_verts=True, **body_param)
verts_full = output.vertices
body_markers_rec = verts_full[:,self.marker,:]
foot_markers_rec = verts_full[:,self.foot_marker,:]
rhand_verts_rec = verts_full[:, self.rhand_verts, :]
############################
# compute normal
mesh = o3d.geometry.TriangleMesh()
verts_full_new = verts_full.detach().cpu().numpy()[0]
mesh.vertices = o3d.utility.Vector3dVector(verts_full_new)
mesh.triangles = o3d.utility.Vector3iVector(self.faces)
mesh.compute_vertex_normals()
normals = np.asarray(mesh.vertex_normals)
rh_normals = torch.tensor(normals[self.rhand_verts, :]).to(torch.float32).to(self.device).view(-1, 778, 3)
##################################################
loss_rec = torch.mean(torch.abs(body_markers_rec-body_markers.detach()))
loss_body_rec = torch.mean(torch.abs(body_markers_rec[:, :49, :]-body_markers.detach()[:, :49, :]))
##################################################
# hand contact loss
o2h_signed, h2o_signed, _ = point2point_signed(rhand_verts_rec, verts_object.float(), rh_normals, normal_object.float())
v_contact = torch.zeros([1, h2o_signed.size(1)]).to(self.device)
v_collision = torch.zeros([1, h2o_signed.size(1)]).to(self.device)
v_dist = (h2o_signed < 0.02) * (h2o_signed > 0) * (self.v_weights2[None] > 0.7)
v_dist_neg = h2o_signed < 0
v_contact[v_dist] = 5 * self.v_weights[None][v_dist] # weight for close vertices
v_collision[v_dist_neg] = 10 # more weight for penetration
w = torch.zeros([1, o2h_signed.size(1)]).to(self.device)
w_dist = (o2h_signed < 0.01) * (o2h_signed > 0)
w_dist_neg = o2h_signed < 0
w[w_dist] = 0 # less weight for far away vertices
w[w_dist_neg] = 20 # more weight for penetration
f = torch.nn.ReLU()
h_contact = 1 * torch.mean(torch.einsum('ij,ij->ij', torch.abs(h2o_signed), v_contact)) # replace with key markers
h_collision = 1 * torch.mean(torch.einsum('ij,ij->ij', torch.abs(h2o_signed), v_collision)) # keep it
loss_dist_o = 1 * torch.mean(torch.einsum('ij,ij->ij', torch.abs(o2h_signed), w)) #
loss_pen = 1 * torch.sum(f(-h2o_signed)**2)
#################################################
# foot loss
loss_foot = 0.1 * torch.mean(torch.abs(foot_markers_rec[:,:,-1]-0.02))
#################################################
# reg loss
loss_vpose_reg = 0.0001*torch.mean(self.vpose_rec**2)
loss_hand_pose_reg = 0.0005*torch.mean(self.hand_pose**2)
loss_eye_pose_reg = 0.0001*torch.mean(self.eye_pose**2)
##################################################
loss = (5 * loss_rec
+ (self.only_rec==False) * (stage>1)* 2 * alpha * (h_contact + h_collision + loss_dist_o) # 2 / 0 / 0
+ (stage>1)*loss_hand_pose_reg
+ (stage>1)*loss_eye_pose_reg
+ (stage>0)*loss_vpose_reg
+ (stage<2)*loss_foot
)
loss_dict = {}
loss_dict['total'] = loss.detach().cpu().numpy()
loss_dict['rec'] = loss_rec.detach().cpu().numpy()
loss_dict['body_rec'] = loss_body_rec.detach().cpu().numpy()
loss_dict['hand contact'] = h_contact.detach().cpu().numpy()
loss_dict['hand collision'] = h_collision.detach().cpu().numpy()
loss_dict['object collision'] = loss_dist_o.detach().cpu().numpy()
loss_dict['foot'] = (stage<2)*loss_foot.detach().cpu().numpy()
loss_dict['reg'] = (loss_vpose_reg+loss_hand_pose_reg+loss_eye_pose_reg).detach().cpu().numpy()
vertices_info = {}
vertices_info['hand colli'] = torch.where(v_dist_neg==True)[0].size()[0]
vertices_info['obj colli'] = torch.where(w_dist_neg==True)[0].size()[0]
vertices_info['contact'] = torch.where((h2o_signed < 0.001) * (h2o_signed > -0.001)==True)[0].size()[0]
return loss, loss_dict, body_markers_rec, body_param, vertices_info
def fitting(self, body_markers, object_contact, markers_contact, verts_object, normal_object, gender, betas=None):
if gender == 'male':
self.bm = self.bm_male
elif gender == 'female':
self.bm = self.bm_female
self.faces = self.bm.faces
# print(self.bm.pose_mean.size())
early_stopping = EarlyStopping(patience=300)
smplxparams_list = []
markers_fit_list = []
best_eval_grasp = 10000
early_stop = False
tmp_info = None
save_loss = {}
save_loss['total'] = []
save_loss['rec'] = []
save_loss['body_rec'] = []
save_loss['hand contact'] = []
save_loss['hand collision'] = []
save_loss['object collision'] = []
save_loss['foot'] = []
save_loss['reg'] = []
save_loss['hand markers colli'] = []
save_loss['hand colli'] = []
save_loss['obj colli'] = []
save_loss['contact'] = []
save_loss['contact map diff'] = []
save_loss['marker contact'] = []
save_loss['object contact'] = []
save_loss['prior contact'] = []
start = time.time()
for ss, optimizer in enumerate(self.optimizers):
for ii in range(self.num_iter[ss]):
alpha = min(ii/self.num_iter[ss]*2, 1)
optimizer.zero_grad()
loss, loss_dict, markers_fit, body_param, vertices_info, rhand_verts_rec, rh_normals, h2o_signed, o2h_signed = self.calc_loss_contact_map(body_markers, verts_object, normal_object, object_contact, markers_contact, gender, betas, ss, alpha)
loss.backward(retain_graph=False)
optimizer.step()
losses_str = ' '.join(['{}: {:.4f} | '.format(x, loss_dict[x]) for x in loss_dict.keys()])
verts_str = ' '.join(['{}: {} | '.format(x, int(vertices_info[x])) for x in vertices_info.keys()])
if self.verbose and not (ii+1) % 50:
self.logger('[INFO][fitting][stage{:d}] iter={:d}, loss:{:s}, verts_info:{:s}'.format(ss,
ii, losses_str, verts_str))
# #### (optional) debug here
# # import open3d as o3d
# import sys
# object_pcd = o3d.geometry.PointCloud()
# rhand_pcd = o3d.geometry.PointCloud()
# object_pcd.points = o3d.utility.Vector3dVector(verts_object.squeeze().detach().cpu().numpy())
# object_pcd.normals = o3d.utility.Vector3dVector(normal_object.squeeze().detach().cpu().numpy())
# rhand_pcd.points = o3d.utility.Vector3dVector(rhand_verts_rec.squeeze().detach().cpu().numpy())
# rhand_pcd.normals = o3d.utility.Vector3dVector(rh_normals.squeeze().detach().cpu().numpy())
# # print(h2o_signed)
# h_in = torch.where(h2o_signed<0)[1].cpu().numpy()
# colors_rh = np.zeros((rhand_verts_rec.shape[1], 3))
# colors_rh[h_in, 0] = 1
# rhand_pcd.colors = o3d.utility.Vector3dVector(colors_rh)
# # print(h2o_signed)
# o_in = torch.where(o2h_signed<0)[1].cpu().numpy()
# print(o_in)
# colors_obj = np.zeros((2048, 3))
# colors_obj[:, 1] = 1
# colors_obj[o_in, 1] = 0
# object_pcd.colors = o3d.utility.Vector3dVector(colors_obj)
# # o3d.visualization.draw_geometries([rhand_pcd, object_pcd])
# # o3d.visualization.draw_geometries([rhand_pcd])
# o3d.visualization.draw_geometries([object_pcd])
eval_grasp = loss
# eval_grasp = vertices_info['hand colli'] + vertices_info['obj colli']#-8*vertices_info['contact']
# contact_num = vertices_info['contact']
for key in loss_dict.keys():
save_loss[key] = save_loss[key] + [loss_dict[key]]
for key in vertices_info.keys():
save_loss[key] = save_loss[key] + [vertices_info[key]]
if self.only_rec != True:
if ss==2 and ii>200 and eval_grasp < best_eval_grasp:# and contact_num>=5:
best_eval_grasp = eval_grasp
tmp_smplxparams = {}
tmp_smplxparams['transl'] = copy.deepcopy(self.transl_rec).detach()
tmp_smplxparams['global_orient'] = RotConverter.rotmat2aa(RotConverter.cont2rotmat(copy.deepcopy(self.glo_rot_rec).detach()))
# smplxparams['betas'] = torch.tensor(betas, dtype=torch.float32, requires_grad=False).repeat(self.batch_size, 1).to(self.device)
tmp_smplxparams['betas'] = copy.deepcopy(self.betas).detach()
tmp_smplxparams['body_pose'] = self.vposer.decode(copy.deepcopy(self.vpose_rec).detach(),
output_type='aa').view(self.batch_size, -1)
tmp_smplxparams['left_hand_pose'] = copy.deepcopy(self.hand_pose).detach()[:,:self.hand_ncomps]
tmp_smplxparams['right_hand_pose'] = copy.deepcopy(self.hand_pose).detach()[:,self.hand_ncomps:]
tmp_smplxparams['leye_pose'] = copy.deepcopy(self.eye_pose).detach()[:,:3]
tmp_smplxparams['reye_pose'] = copy.deepcopy(self.eye_pose).detach()[:,3:]
tmp_markers_fit = markers_fit
tmp_info = '[stage{:d}] iter={:d}, loss:{:s}, verts_info:{:s}'.format(ss,
ii, losses_str, verts_str)
if self.verbose:
self.logger('saving:{}'.format(tmp_info))
if ss==2 and ii>200:
if early_stopping(eval_grasp):
# print(early_stopping.counter)
if contact_num < 4:
early_stopping.counter = 0
else:
early_stop = True
self.logger('Early stop...')
self.logger('Save %s' % tmp_info)
break
if ss==2 and ii==self.num_iter[ss]-1:
early_stop = True
self.logger('Save %s' % tmp_info)
# early_stop = False
if not early_stop or tmp_info is None:
# self.logger('No EARLY STOP!')
smplxparams = {}
smplxparams['transl'] = copy.deepcopy(self.transl_rec).detach()
smplxparams['global_orient'] = RotConverter.rotmat2aa(RotConverter.cont2rotmat(copy.deepcopy(self.glo_rot_rec).detach()))
smplxparams['betas'] = copy.deepcopy(self.betas).detach()
smplxparams['body_pose'] = self.vposer.decode(copy.deepcopy(self.vpose_rec).detach(),
output_type='aa').view(self.batch_size, -1)
smplxparams['left_hand_pose'] = copy.deepcopy(self.hand_pose).detach()[:,:self.hand_ncomps]
smplxparams['right_hand_pose'] = copy.deepcopy(self.hand_pose).detach()[:,self.hand_ncomps:]
smplxparams['leye_pose'] = copy.deepcopy(self.eye_pose).detach()[:,:3]
smplxparams['reye_pose'] = copy.deepcopy(self.eye_pose).detach()[:,3:]
# print('handpose:', self.hand_pose)
# print(smplxparams['right_hand_pose'])
# smplx_copy = copy.deepcopy(smplxparams)
### TO FIX THE bug
smplxparams_list.append(smplxparams)
markers_fit_list.append(markers_fit.detach().cpu().numpy()[0])
else:
smplxparams_list.append(tmp_smplxparams)
markers_fit_list.append(tmp_markers_fit.detach().cpu().numpy()[0])
# self.logger('beta after fitting: %s' % str(smplxparams['betas']))
# time_0 = time.time() - start
# self.logger('time per sample: %f' % time_0)
self.reset()
for key in save_loss.keys():
save_loss[key] = np.asarray(save_loss[key])
# print(save_loss[key].shape)
return markers_fit_list, smplxparams_list, save_loss