[ECCV 2020] Official PyTorch Implementation of "DLow: Diversifying Latent Flows for Diverse Human Motion Prediction". ECCV 2020.
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]
data_3d_h36m.npz
(Human3.6M) and data_3d_humaneva15.npz
(HumanEva-I) under the data
folder.results
folder inside the root of this repo.We have provided 4 example YAML configs inside motion_pred/cfg
:
h36m_nsamp10.yml
and h36m_nsamp50.yml
for Human3.6M for number of samples 10 and 50 respectively.humaneva_nsamp10.yml
and humaneva_nsamp50.yml
for HumanEva-I for number of samples 10 and 50 respectively.results
.python motion_pred/exp_vae.py --cfg h36m_nsamp10
python motion_pred/exp_dlow.py --cfg h36m_nsamp10
python motion_pred/eval.py --cfg h36m_nsamp10 --mode vis
Useful keyboard shortcuts for the visualization GUI:
Key | Functionality |
---|---|
d | test next motion data |
c | save current animation as out/video.mp4 |
space | stop/resume animation |
1 | show DLow motion samples |
2 | show VAE motion samples |
python motion_pred/eval.py --cfg h36m_nsamp50 --mode stats
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}
}
Part of the code is borrowed from the VideoPose3D repo.
The software in this repo is freely available for free non-commercial use. Please see the license for further details.