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---
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},
}
``` |