Shape Generation and Completion Through Point-Voxel Diffusion

Project | Paper

Implementation of

Pretrained Models

Pretrained models can be accessed here.

Requirements:

Make sure the following environments are installed.

python==3.6
pytorch==1.4.0
torchvision==0.5.0
cudatoolkit==10.1
matplotlib==2.2.5
tqdm==4.32.1
open3d==0.9.0

The code was tested on Unbuntu with Titan RTX.

Training on CIFAR-10:

$ python train_cifar.py

Please refer to the python file for optimal training parameters.

Results

Some generative results are as follows.

Reference

@inproceedings{han2020joint,
  title={Joint Training of Variational Auto-Encoder and Latent Energy-Based Model},
  author={Han, Tian and Nijkamp, Erik and Zhou, Linqi and Pang, Bo and Zhu, Song-Chun and Wu, Ying Nian},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7978--7987},
  year={2020}
}

Acknowledgement

For any questions related to codes and experiment setting, please contact Linqi (Alex) Zhou (alexzhou907@gmail.com). For questions related to model and algorithm in the paper, please contact Tian Han (hantian@ucla.edu). Thanks to @Tian Han and @Erik Njikamp for their colloboration and guidance.