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Volumetric Heatmap Autoencoder

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 the Volumetri Heatmap Autencoder on JTA dataset. Here you can also find the code for training the full pipeline.

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
• Modify the conf/default.yaml configuration file specifying the path to the JTA dataset directory
  • JTA_PATH: <your_jta_path>

Show Paper Results

• Modify the conf/pretrained.yaml configuration file specifying the path to the JTA dataset directory
  • JTA_PATH: <your_jta_path>
• run python show.py pretrained (python >= 3.6)

Train

• run python main.py default (python >= 3.6)

TIP: training using sparse ground truth is not trivial since the network will output maps with only zeros no matter what. A practical way to overcome this problem is to start the training with sigma 8 and then halve the sigma whenever you encounter a loss plateaus. In our experiments we stopped when fully trained at sigma 2.

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}
 }