[R][P] StarGAN v2: Diverse Image Synthesis for Multiple Domains
![img](ypy74yikfs241 “Diverse image synthesis results on the CelebA-HQ dataset and our newly collected animal faces (AFHQ) dataset. The first column shows input images while the remaining columns are images synthesized by StarGAN v2.”)
!(pdaoqt5rfs241 “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 at the link below.”)
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.