H2SW

Official PyTorch implementation for paper: Hierarchical Hybrid Sliced Wasserstein: A Scalable Metric for Heterogeneous Joint Distributions

Details of the model architecture and experimental results can be found in our papers.

@article{nguyen2024h2sw,
  title={Hierarchical Hybrid Sliced Wasserstein: A Scalable Metric for Heterogeneous Joint Distributions},
  author={Khai Nguyen and Nhat Ho},
  journal={Advances in Neural Information Processing Systems},
  year={2024},
  pdf={https://arxiv.org/pdf/2404.15378}
}

Please CITE our paper whenever this repository is used to help produce published results or incorporated into other software.

This implementation is made by Khai Nguyen.

Requirements

To install the required python packages, run

pip install -r requirements.txt

What is included?

• 3D Mesh Gradient flow
• 3D Mesh Autoencoder

Point-Cloud Gradient flow

cd GradientFlow
python armadillo.py;
python bunny.py

3D Mesh Autoencoder

Please read the README file in the MeshAE folder.