StarGAN v2: Diverse Image Synthesis for Multiple Domains

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 mo...

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Bibliographic Details
Published in2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 8185 - 8194
Main Authors Choi, Yunjey, Uh, Youngjung, Yoo, Jaejun, Ha, Jung-Woo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2020
Subjects
Animals
Generators
Image generation
Image reconstruction
Training
Transforms
Visualization
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Summary: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 differences. The code, pretrained models, and dataset are available at https://github.com/clovaai/stargan-v2.
ISSN:2575-7075
DOI:10.1109/CVPR42600.2020.00821