Data Preparation, Processing, and Visualization for MoVi Data

MoVi-Toolbox

Data Preparation, Processing, and Visualization for MoVi Data, https://www.biomotionlab.ca/movi/

MoVi-Toolbox

MoVi is a large multipurpose dataset of human motion and video.

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

Installation

Requirements

Python 3.*
MATLAB v>2017

In case you are interested in using body shape data (or also AMASS/MoVi original data) follow the instructions on AMASS Github page.

Tutorials

We have provided very brief tutorials on how to use the dataset in MoCap. Some of the functions are only provided in MATLAB or Python so please take a look at both tutorial files tutorial_MATLAB.m and tutorial_python.ipynb.
The tutorial on how to have access to the dataset is given here.

Important Notes

The video data for each round are provided as a single sequence (and not individual motions). In case you are interested in having synchronized video and AMASS (joint and body) data, you should trim F_PGx_Subject_x_L.avi files into single motion video files using single_videos.m function.
The timestamps (which separate motions) are provided by the name of “flags” in V3D files (only for f and s rounds). Please notice that “flags30” can be used for video data and “flags120” can be used for mocap data. The reason for having two types of flags is that video data were recorded in 30 fps and mocap data were recorded in 120 fps.
The body mesh is not provided in AMASS files by default. Please use amass_fk function to augment AMASS data with the corresponding body mesh (vertices). (the details are explained in the tutorial_python.ipynb)

Citation

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

@misc{ghorbani2020movi,
    title={MoVi: A Large Multipurpose Motion and Video Dataset},
    author={Saeed Ghorbani and Kimia Mahdaviani and Anne Thaler and Konrad Kording and Douglas James Cook and Gunnar Blohm and Nikolaus F. Troje},
    year={2020},
    eprint={2003.01888},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}