zhaoyang10 / stargan-v2

StarGAN v2 - Official PyTorch Implementation

StarGAN v2 - Official PyTorch Implementation [Paper] [Video]

This repository provides the official PyTorch implementation of the following paper:

StarGAN v2: Diverse Image Synthesis for Multiple Domains
Yunjey Choi*, Youngjung Uh*, Jaejun Yoo*, Jung-Woo Ha
Clova AI Research, NAVER Corp. (* indicates equal contribution)
https://arxiv.org/abs/1912.01865

Abstract: A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. Existing methods address either of the issues, having limited diversity or multiple models for all domains. We propose StarGAN v2, a single framework that tackles both and shows significantly improved results over the baselines. Experiments on CelebA-HQ and a new animal faces dataset (AFHQ) validate our superiority in terms of visual quality, diversity, and scalability. To better assess image-to-image translation models, we release AFHQ, high-quality animal faces with large inter- and intra-domain variations. The code, pretrained models, and dataset will be released for reproducibility.

Results

StarGAN v2 can transform a source image into an output image reflecting the style (e.g., hairstyle and makeup) of a given reference image. Additional high-quality videos can be found here.

License

The source code, pretrained models, and dataset will be available under Creative Commons BY-NC 4.0 license by NAVER Corporation. You can use, copy, tranform and build upon the material for non-commercial purposes as long as you give appropriate credit by citing our paper, and indicate if changes were made.

Installation

The code and usage examples will be updated soon. Please stay tuned.

Citation

If you find this work is useful for your research, please cite our paper:

@article{choi2019starganv2,
  title={StarGAN v2: Diverse Image Synthesis for Multiple Domains},
  author={Yunjey Choi and Youngjung Uh and Jaejun Yoo and Jung-Woo Ha},
  journal={arXiv preprint arXiv:1912.01865},
  year={2019}
}