---
license: other

tags:
- Shape modeling
- Volumetric models

datasets:
- shapenet
---

### Model Description

- SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation
- Zheng, Xin-Yang and Liu, Yang and Wang, Peng-Shuai and Tong, Xin, 2022

The proposed deeplearning model for 3D shape generation called signed distance field (SDF) - SDF-StyleGAN, whicH is based on StyleGAN2. The goal of this approach is to minimize the visual and geometric differences between the generated shapes and a collection of existing shapes.


### Documents

- [GitHub Repo](https://github.com/Zhengxinyang/SDF-StyleGAN)
- [Paper - SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation](https://arxiv.org/pdf/2206.12055.pdf)
  
### Datasets

ShapeNet is a comprehensive 3D shape dataset created for research in computer graphics, computer vision, robotics and related diciplines.


- [Offical Dataset of ShapeNet](https://shapenet.org/)
- [author's data preparation script](https://github.com/Zhengxinyang/SDF-StyleGAN)
- [author's training data](https://pan.baidu.com/s/1nVS7wlcOz62nYBgjp_M8Yg?pwd=oj1b)


### How to use

Training snippets are published under the official GitHub repository above.

### BibTeX Entry and Citation Info

 ```
@inproceedings{zheng2022sdfstylegan,
  title = {SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation},
  author = {Zheng, Xin-Yang and Liu, Yang and Wang, Peng-Shuai and Tong, Xin},
  booktitle = {Comput. Graph. Forum (SGP)},
  year = {2022},
}
 ```