RVH Mesh registration repository

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.

Contents

1. Dependencies
2. Prepare model files
3. Different registration methods

Dependencies

Most dependencies are included in requirement.txt file, the following modules need to be installed manually:

1. MPI-IS Mesh library. See installation here.
2. Openpose library. See lifting 2D poses.

Prepare model files

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.

Different registration methods

We provide various methods for registering SMPL or SMPL+H to scans or point clouds:

1. Fit SMPL(H) to scans
2. Fit SMPL(H)+D to scans
3. Fit SMPL(H) to point clouds
4. 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.

Fit SMPL(H) to scans

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.

Fit SMPL(H)+D model to scans

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/ 

Fit SMPL(H) model to Kinect point clouds

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

Fit SMPL(H)+D model to scans using IP-Net

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

Cite us:

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},
}

License

Copyright (c) 2022 Bharat Lal Bhatnagar, Max-Planck-Gesellschaft

Please read carefully the following terms and conditions and any accompanying documentation before you download and/or use this software and associated documentation files (the "Software").

The authors hereby grant you a non-exclusive, non-transferable, free of charge right to copy, modify, merge, publish, distribute, and sublicense the Software for the sole purpose of performing non-commercial scientific research, non-commercial education, or non-commercial artistic projects.

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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. You agree to cite the Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction paper in documents and papers that report on research using this Software.