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. I will add code and models for Panoptic and Human3.6m soon.

Some Results

Input	Prediction

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

Compile Cuda Kernel

cd into the folder nms3d and run python 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 directory
- JTA_PATH: <your_jta_path>

Train

run python train.py --exp_name=default (python >= 3.6)

Show Visual Results

run python show.py --exp_name=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 directory
- JTA_PATH: <your_jta_path>
run python show.py --exp_name=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}
 }

Guanghan/LoCO