AMASS: Archive of Motion Capture as Surface Shapes

AMASS is a large database of human motion unifying different optical marker-based motion capture datasets by representing them within a common framework and parameterization. AMASS is readily useful for animation, visualization, and generating training data for deep learning.

Here we provide tools and tutorials to use AMASS in your research projects. More specifically:

Following the recommended splits of data by AMASS, we provide three non-overlapping train/validation/test splits.
AMASS uses an extended version of SMPL+H with DMPLs. Here we show how to load different components and visualize a body model with AMASS data.
AMASS is also compatible with SMPL and SMPL-X body models. We show how to use the body data from AMASS to animate these models.

Installation
Body Models
Tutorials
Citation
License
Contact

Installation

Requirements

Python 3.7
Human Body Prior
Pyrender for visualizations

Install from this repository for the latest developments:

pip install git+https://github.com/nghorbani/configer
pip install git+https://github.com/nghorbani/human_body_prior
pip install git+https://github.com/nghorbani/amass

Body Models

AMASS fits a statistical body model to labeled marker-based optical motion capture data. In the paper originally we use SMPL+H with extended shape space, e.g. 16 betas, and DMPLs. Please download each and put them in body_models folder of this repository after you obtained the code from GitHub.

Tutorials

We release tools and multiple Jupyter notebooks to demonstrate how to use AMASS to animate SMPLH body model.

Furthermore, as promised in the supplementary material of the paper, we release code to produce synthetic mocap using DFaust registrations.

Please refer to tutorials for further details.

Citation

Please cite the following paper if you use this code directly or indirectly in your research/projects:

@inproceedings{AMASS:2019,
  title={AMASS: Archive of Motion Capture as Surface Shapes},
  author={Mahmood, Naureen and Ghorbani, Nima and F. Troje, Nikolaus and Pons-Moll, Gerard and Black, Michael J.},
  booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
  year={2019},
  month = {Oct},
  url = {https://amass.is.tue.mpg.de},
  month_numeric = {10}
}

License

Software Copyright License for non-commercial scientific research purposes. Please read carefully the terms and conditions and any accompanying documentation before you download and/or use the AMASS dataset, and software, (the "Model & Software"). By downloading and/or using the Model & Software (including downloading, cloning, installing, and any other use of this GitHub repository), you acknowledge that you have read these terms and conditions, understand them, and agree to be bound by them. If you do not agree with these terms and conditions, you must not download and/or use the Model & Software. Any infringement of the terms of this agreement will automatically terminate your rights under this License.

Contact

The code in this repository is developed by Nima Ghorbani.

If you have any questions you can contact us at amass@tuebingen.mpg.de.

For commercial licensing, contact ps-licensing@tue.mpg.de

McDo/amass