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DLow: Diversifying Latent FLows

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This repo contains the official implementation of our paper:

DLow: Diversifying Latent Flows for Diverse Human Motion Prediction
Ye Yuan, Kris Kitani
ECCV 2020
[website] [paper] [talk] [summary] [demo]

Installation

Datasets

Environment

Pretrained Models

Train

Configs

We have provided 4 example YAML configs inside motion_pred/cfg:

Train VAE

python motion_pred/exp_vae.py --cfg h36m_nsamp10

Train DLow (After VAE is trained)

python motion_pred/exp_dlow.py --cfg h36m_nsamp10

Test

Visualize Motion Samples

python motion_pred/eval.py --cfg h36m_nsamp10 --mode vis

Useful keyboard shortcuts for the visualization GUI:

KeyFunctionality
dtest next motion data
csave current animation as out/video.mp4
spacestop/resume animation
1show DLow motion samples
2show VAE motion samples

Compute Metrics

python motion_pred/eval.py --cfg h36m_nsamp50 --mode stats

Citation

If you find our work useful in your research, please cite our paper DLow:

@inproceedings{yuan2020dlow,
    title={Dlow: Diversifying latent flows for diverse human motion prediction},
    author={Yuan, Ye and Kitani, Kris},
    booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
    year={2020}
}

Acknowledgement

Part of the code is borrowed from the VideoPose3D repo.

License

The software in this repo is freely available for free non-commercial use. Please see the license for further details.