StarGAN v2: Diverse Image Synthesis for Multiple Domains
Open accessAbstract
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.
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StarGAN2 simultaneously satisfies diversity of generated images and scalability over multiple domains in image-to-image translation.
“StarGAN2 (Choi et al. 2020) simultaneously satisfies two properties in image-to-image translation: diversity of generated images as well as scalability over multiple domains.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Choi et al. 2020
Unlike the original StarGAN, which could only perform one-to-one translation between each pair of domains, StarGANv2 can handle multiple domains simultaneously.
“Unlike the original StarGAN [44], which could only perform one-to-one translation between each pair of domains, StarGANv2 [43] can handle multiple domains simultaneously.”
From Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions (2023) · cites this paper as [43]
StarGANv2 can perform multi-domain image-to-image translation, enabling generation of images across multiple different domains using a single unified model.
“Another technique is StarGANv2 [43], which is able to perform multi-domain image-to-image translation, enabling the generation of images across multiple different domains using a single unified model.”
From Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions (2023) · cites this paper as [43]
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- Authors
- Choi, Y., Uh, Y., Yoo, J., & Ha, J.-W.
- Affiliations
- NAVER Cloud (South Korea) (KR)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 8185-8194
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3034600949
- Cited by
- 1,663
- References
- 39
- Citations by year
- 2026: 107 · 2025: 222 · 2024: 328 · 2023: 418 · 2022: 262 · 2021: 252 · 2020: 73 · 2019: 1
- FWCI
- 77.50
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- image-to-image translation, domain adaptation, generative adversarial networks, image diversity, CelebA-HQ, visual quality
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