Learning on Compressed Output (LoCO)
Accepted to CVPR 2020
This repo contains the code related to the paper Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimation accepted to CVPR 2020 with the instructions for training and testing our models on the JTA dataset. Here you can also find the code for training the Volumetric Heatmap Autoencoder.
Some Results
Input | Prediction |
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Quick Demo
- run
python demo.py --ex=1
(python >= 3.6)- please wait some seconds: it will display some precomputed results. You can change the
ex
number from 1 to 3 to see different results
- please wait some seconds: it will display some precomputed results. You can change the
Compile Cuda Kernel
cd
into the foldernms3d
and runpython setup.py install
(python >= 3.6). Make sure to add your cuda directory to your environment variables.
Intructions
- Download the JTA dataset
in
<your_jta_path>
- Run
python to_poses.py --out_dir_path='poses' --format='torch'
(link) to generate the<your_jta_path>/poses
directory - Run
python to_imgs.py --out_dir_path='frames' --img_format='jpg'
(link) to generate the<your_jta_path>/frames
directory - Download our precomputed codes from here
and unzip them into
<your_jta_path>
- Modify the
conf/default.yaml
configuration file specifying the path to the JTA dataset directoryJTA_PATH: <your_jta_path>
Train
- run
python main.py default
(python >= 3.6)
Show Visual Results
- run
python show.py default
(python >= 3.6)- Note that, before showing the results, you must have completed at least one training epoch; however, to achieve results comparable to those reported in the paper, it is advisable to carry out a training of at least 100 epochs
Show Paper Results
- Download the pretrained weights and extract them into the project folder
- Modify the
conf/pretrained.yaml
configuration file specifying the path to the JTA dataset directoryJTA_PATH: <your_jta_path>
- run
python show.py pretrained
(python >= 3.6)
Citation
We believe in open research and we are happy if you find this data useful.
If you use it, please cite our work.
@inproceedings{fabbri2020compressed,
title = {Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimation},
author = {Fabbri, Matteo and Lanzi, Fabio and Calderara, Simone and Alletto, Stefano and Cucchiara, Rita},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2020}
}