readme: ---
  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},
  }
   ```