Single person estimation on smplx
Young2647 opened this issue · comments
Thanks for your great work!
I tried your demo on single person estimation and it works well. However, it is using smpl for estimation. I wonder how I can change the model from smpl to smplx. How should I modify the scripts? Thanks!
Hi @Young2647, welcome to commit your first issue!
你好 @Young2647，非常欢迎首次提交你的问题！
We have added support for SMPL-X estimation in our latest release v0.7.0. You may use the feature by changing the config to SMPLifyX here with SMPLX body model specified.
In addition, make sure to set the extra poses and body weights accordingly in the optimization stages config when using SMPLifyX.
You may try out and adjust based on this configuration for SMPLify-X:
type = 'MultiViewSinglePersonSMPLEstimator'
work_dir = './temp'
verbose = True
logger = None
bbox_detector = None
kps2d_estimator = None
smplify = dict(
type='SMPLifyX',
verbose=verbose,
info_level='stage',
n_epochs=1,
use_one_betas_per_video=True,
hooks=[
dict(type='SMPLifyVerboseHook'),
],
grad_clip=3.0,
logger=logger,
body_model=dict(
type='SMPLX',
gender='neutral',
num_betas=10,
keypoint_convention='smplx',
model_path='mmhuman3d/data/body_models/smplx',
batch_size=1,
use_face_contour=True,
use_pca=False,
num_pca_comps=24,
flat_hand_mean=False,
logger=logger),
optimizer=dict(
type='LBFGS', max_iter=20, lr=1.0, line_search_fn='strong_wolfe'),
ignore_keypoints=[
'right_smalltoe', 'right_bigtoe', 'left_smalltoe', 'left_bigtoe'
],
handlers=[
dict(
handler_key='keypoints3d_mse',
type='Keypoint3dMSEHandler',
mse_loss=dict(
type='KeypointMSELoss',
loss_weight=10.0,
reduction='sum',
sigma=100),
logger=logger),
dict(
handler_key='keypoints2d_mse',
type='MultiviewKeypoint2dMSEHandler',
mse_loss=dict(
type='KeypointMSELoss',
loss_weight=1.0,
reduction='sum',
sigma=100),
logger=logger),
dict(
handler_key='shape_prior',
type='BetasPriorHandler',
prior_loss=dict(
type='ShapePriorLoss',
loss_weight=5e-3,
reduction='mean'),
logger=logger),
dict(
handler_key='joint_prior',
type='BodyPosePriorHandler',
prior_loss=dict(
type='JointPriorLoss',
loss_weight=1.0,
reduction='mean',
use_full_body=True,
smooth_spine=False,
smooth_spine_loss_weight=0.0,
lock_foot=False,
lock_foot_loss_weight=1.0,
lock_apose_spine=False,
lock_apose_spine_loss_weight=1.0),
logger=logger),
dict(
handler_key='smooth_joint',
type='BodyPosePriorHandler',
prior_loss=dict(
type='SmoothJointLoss',
loss_weight=1.0,
reduction='mean',
loss_func='L2'),
logger=logger),
dict(
handler_key='pose_reg',
type='BodyPosePriorHandler',
prior_loss=dict(
type='PoseRegLoss', loss_weight=0.001, reduction='mean'),
logger=logger),
dict(
handler_key='keypoints3d_limb_len',
type='Keypoint3dLimbLenHandler',
loss=dict(
type='LimbLengthLoss',
convention='smplx',
loss_weight=1.0,
reduction='mean'),
logger=logger),
],
stages=[
# stage 0 betas
dict(
n_iter=10,
ftol=1e-4,
fit_global_orient=False,
fit_transl=False,
fit_body_pose=False,
fit_betas=True,
fit_left_hand_pose=False,
fit_right_hand_pose=False,
fit_expression=False,
fit_jaw_pose=False,
fit_leye_pose=False,
fit_reye_pose=False,
keypoints3d_mse_weight=0.0,
keypoints2d_mse_weight=0.0,
keypoints3d_limb_len_weight=0.5,
shape_prior_weight=5e-3,
joint_prior_weight=0.0,
smooth_joint_weight=0.0,
pose_reg_weight=0.0,
pose_prior_weight=0.0),
# stage 1 global orient, transl, betas
dict(
n_iter=50,
ftol=1e-4,
fit_global_orient=True,
fit_transl=True,
fit_body_pose=False,
fit_betas=False,
fit_left_hand_pose=False,
fit_right_hand_pose=False,
fit_expression=False,
fit_jaw_pose=False,
fit_leye_pose=False,
fit_reye_pose=False,
keypoints3d_mse_weight=1.0,
keypoints2d_mse_weight=0.0,
keypoints3d_limb_len_weight=0.0,
shape_prior_weight=0.0,
joint_prior_weight=0.0,
smooth_joint_weight=0.0,
pose_reg_weight=0.0,
pose_prior_weight=0.0,
shoulder_weight=1.0,
hip_weight=1.0,
body_weight=0.0,
hand_weight=0.0,
face_weight=0.0,
foot_weight=0.0),
# stage 2 pose: fit pose based on kps
dict(
n_iter=40,
ftol=1e-4,
fit_global_orient=True,
fit_transl=True,
fit_body_pose=True,
fit_betas=False,
fit_left_hand_pose=False,
fit_right_hand_pose=False,
fit_expression=False,
fit_jaw_pose=False,
fit_leye_pose=False,
fit_reye_pose=False,
keypoints3d_mse_weight=1.0,
keypoints2d_mse_weight=0.0,
keypoints3d_limb_len_weight=0.0,
shape_prior_weight=0.1,
joint_prior_weight=0.0,
smooth_joint_weight=0.1,
pose_reg_weight=0.001,
pose_prior_weight=0.0,
shoulder_weight=2.0,
hip_weight=1.0,
body_weight=1.0,
hand_weight=0.5,
face_weight=0.5,
foot_weight=1.0),
],
)
triangulator = None
cam_pre_selector = None
cam_selector = None
final_selectors = None
kps3d_optimizers = None
kps3d_optimizers = [
dict(type='NanInterpolation',
verbose=verbose,
logger=logger),
]
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