This repository collects methods to register SMPL model to point clouds or 3D scans.
If you use this code please cite:
Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction, ECCV'20.
LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration, NeurIPS'20.
Most dependencies are included in requirement.txt file, the following modules need to be installed manually:
- MPI-IS Mesh library. See installation here.
- Openpose library. See lifting 2D poses.
We provide SMPL or SMPL+H registration in this repo. Depending on your model choice, you should prepare the corresponding model files, for details please check here.
We provide various methods for registering SMPL or SMPL+H to scans or point clouds:
- Fit SMPL(H) to scans
- Fit SMPL(H)+D to scans
- Fit SMPL(H) to point clouds
- Fit SMPL(H)+D to point clouds using IP-Net
The default model we use is SMPL. In all the following scripts, you can switch to SMPL-H model by adding a command option -hands.
For more accurate registration, we recommend to first obtain 3D body keypoints from scans using openpose and optimization. See details here.
With the model files and 3D keypoints ready, you can run fitting with:
python smpl_registration/fit_SMPLH.py [scan_path] [pose_file] [save_path]
[-gender male/female]
[-hands optional: use SMPL-H]
Example command using our sample data:
python smpl_registration/fit_SMPLH.py data/mesh_1/scan.obj data/mesh_1/3D_pose.json data/mesh_1/
You can also add option --display to visualize the registration process if you have access to local monitor.
Fitting SMPLH+D is based on fitting SMPLH, hence the command is very similar, except you can provide existing SMPLH parameters as input.
python smpl_registration/fit_SMPLH+D.py [scan_path] [pose_file] [save_path]
[-smpl_pkl existing SMPLH parameters]
[-gender male/female]
[-hands optional: use SMPL-H]
Example command using our sample data:
python smpl_registration/fit_SMPLHD.py data/mesh_1/scan.obj data/mesh_1/3D_pose.json data/mesh_1/
The fitting procedure is very similar to scan fitting. But Kinect point clouds are noisy and incomplete and the person pose captured by Kinects can be much more diverse than scans, we recommend to provide 3d pose estimation to initialize the SMPL model. These initial pose estimations can be obtained from monocular pose estimation methods, for example, FrankMocap.
Also you can obtain 3D joints following instructions here.
Run fitting:
python smpl_registration/fit_SMPLH_pcloud.py [pc_path] [j3d_file] [save_path] [pose_init]
[-gender male/female]
[-hands optional: use SMPL-H]
Example command using our sample data:
python smpl_registration/fit_SMPLH_pcloud.py data/pc/person.ply data/pc/3D_pose.json data/pc/ data/pc/mocap.json
This fitting is based on the IP-Net project. You can download the pretrained IP-Net model here.
This method does not require providing lifted 3D pose. Run fitting:
python smpl_registration/fit_SMPLH_IPNet.py [pc_path] [save path]
[-w checkpoint path]
[-gender male/female]
[-hands optional: use SMPL-H]
Example command using our sample data:
python smpl_registration/fit_SMPLH_IPNet.py data/mesh_1/scan.obj data/mesh_1 -w CHECKPOINT_PATH
If you use this code please cite:
@inproceedings{bhatnagar2020ipnet,
title = {Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction},
author = {Bhatnagar, Bharat Lal and Sminchisescu, Cristian and Theobalt, Christian and Pons-Moll, Gerard},
booktitle = {European Conference on Computer Vision ({ECCV})},
month = {aug},
organization = {{Springer}},
year = {2020},
}
@inproceedings{bhatnagar2020loopreg,
title = {LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration},
author = {Bhatnagar, Bharat Lal and Sminchisescu, Cristian and Theobalt, Christian and Pons-Moll, Gerard},
booktitle = {Advances in Neural Information Processing Systems ({NeurIPS})},
month = {December},
year = {2020},
}
Copyright (c) 2022 Bharat Lal Bhatnagar, Max-Planck-Gesellschaft
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