Awesome

Motron

Hi, and welcome to the codebase for our CVPR22 submission. Feel free to explore one of our pre-trained models using one of the notebooks in the "notebooks" folder. For example re-run the evaluation on H3.6M in the "RES H3.6M Deterministic Evaluation.ipynb" notebook.

Or train your own model via

PYTHONPATH=. python -O h36m/train.py --device cpu --config ./config/h36m.yaml

Model configuration is passed via the YAML file located in the "config" folder.

Install Instructions

Please download the processed H3.6M dataset from here and place it in

./data/processed/h3.6m.npz

Install the requirements

pip install -r requirements.txt

If your device supports GPU acceleration please uncomment line 5 in requirements.txt.

To visualize AMASS output please additionally install

git+https://github.com/nghorbani/configer@5f2b82e5
git+https://github.com/nghorbani/human_body_prior@1936f38
git+https://github.com/nghorbani/amass@e5f0889

The main results can be found here and here.

Preprocess Data

H36M

To download and preprocess the H36M dataset automatically run

 PYTHONPATH=. python ./preprocess/h36m.py

AMASS

Download the AMASS datasets you want to use from https://amass.is.tue.mpg.de

To preprocess run

 PYTHONPATH=. python ./preprocess/amass.py --path [path to amass datasets] --out ./data/processed/amass --datasets [List of datasets to process]

In the paper we eventually also follow the amass evaluation procedure of Aksan et al.. For the future we suggest to use their evaluation data/split as the distribution difference between train and test data is more reasonable.

Cite Us

@article{salzmann2022motron,
  title={Motron: Multimodal Probabilistic Human Motion Forecasting},
  author={Salzmann, Tim and Pavone, Marco and Ryll, Markus},
  journal={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}

If you use data from AMASS or H36M, please cite the original papers as detailed on their website.