|
--- |
|
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}, |
|
} |
|
``` |
