Awesome
human-motion-prediction
This is a pytorch implementation of the paper
Julieta Martinez, Michael J. Black, Javier Romero. On human motion prediction using recurrent neural networks. In CVPR 17.
It can be found on arxiv as well: https://arxiv.org/pdf/1705.02445.pdf
The code in the original repository was written by Julieta Martinez and Javier Romero and is accessible here.
If you have any comment on this fork you can email me at [enriccorona93@gmail.com]
Dependencies
Get this code and the data
First things first, clone this repo and get the human3.6m dataset on exponential map format.
git clone https://github.com/enriccorona/human-motion-prediction-pytorch.git
cd human-motion-prediction-pytorch
mkdir data
cd data
wget http://www.cs.stanford.edu/people/ashesh/h3.6m.zip
unzip h3.6m.zip
rm h3.6m.zip
cd ..
Quick demo and visualization
The code in this fork should work exactly as in the original repo:
For a quick demo, you can train for a few iterations and visualize the outputs of your model.
To train, run
python src/translate.py --action walking --seq_length_out 25 --iterations 10000
To save some samples of the model, run
python src/translate.py --action walking --seq_length_out 25 --iterations 10000 --sample --load 10000
Finally, to visualize the samples run
python src/forward_kinematics.py
This should create a visualization similar to this one
<p align="center"> <img src="https://raw.githubusercontent.com/una-dinosauria/human-motion-prediction/master/imgs/walking.gif"><br><br> </p>Running average baselines
To reproduce the running average baseline results from our paper, run
python src/baselines.py
RNN models
To train and reproduce the results of our models, use the following commands
| model | arguments | training time (gtx 1080) | notes |
|---|---|---|---|
| Sampling-based loss (SA) | python src/translate.py --action walking --seq_length_out 25 | 45s / 1000 iters | Realistic long-term motion, loss computed over 1 second. |
| Residual (SA) | python src/translate.py --residual_velocities --action walking | 35s / 1000 iters | |
| Residual unsup. (MA) | python src/translate.py --residual_velocities --learning_rate 0.005 --omit_one_hot | 65s / 1000 iters | |
| Residual sup. (MA) | python src/translate.py --residual_velocities --learning_rate 0.005 | 65s / 1000 iters | best quantitative. |
| Untied | python src/translate.py --residual_velocities --learning_rate 0.005 --architecture basic | 70s / 1000 iters |
You can substitute the --action walking parameter for any action in
["directions", "discussion", "eating", "greeting", "phoning",
"posing", "purchases", "sitting", "sittingdown", "smoking",
"takingphoto", "waiting", "walking", "walkingdog", "walkingtogether"]
or --action all (default) to train on all actions.
Citing
If you use our code, please cite our work
@inproceedings{julieta2017motion,
title={On human motion prediction using recurrent neural networks},
author={Martinez, Julieta and Black, Michael J. and Romero, Javier},
booktitle={CVPR},
year={2017}
}
Acknowledgments
The pre-processed human 3.6m dataset and some of our evaluation code (specially under src/data_utils.py) was ported/adapted from SRNN by @asheshjain399.
Licence
MIT