Dai, Yihua, Xiang, Tianyi, Deng, Bailin  ORCID: https://orcid.org/0000-0002-0158-7670, Du, Yong, Cai, Hongmin, Qin, Jing and He, Shengfeng
2025.
StyleGAN-∞: extending StyleGAN to arbitrary-ratio translation with StyleBook.
IEEE Transactions on Visualization and Computer Graphics
31
(9)
, pp. 6575-6587.
10.1109/tvcg.2024.3522565
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Abstract
Although pre-trained large-scale generative models StyleGAN series have proven to be effective in various editing and translation tasks, they are limited to pre-defined fixed aspect ratio. To overcome this limitation, we propose StyleGAN-∞, a model that enables pre-trained StyleGAN to perform arbitrary-ratio conditional synthesis. Our key insight is to distill the expressive StyleGAN features into a StyleBook, such that an arbitrary-ratio condition can be translated to other forms by properly assembling pre-defined StyleBook vectors. To learn and leverage the StyleBook, we employ a network with three distinct stages, each corresponding to StyleBook extraction, StyleBook correspondence learning, and arbitrary-ratio synthesis. Extensive experiments on various conditional synthesis tasks, like super-resolution, sketch synthesis, and semantic synthesis, demonstrate superior performances over state-of-the-art image-to-image translation methods. Moreover, our model can easily generate megapixel images in diverse modalities by taking advantage of different pre-trained StyleGAN models.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Publisher: | Institute of Electrical and Electronics Engineers |
| ISSN: | 1077-2626 |
| Date of First Compliant Deposit: | 24 December 2024 |
| Date of Acceptance: | 11 December 2024 |
| Last Modified: | 10 Sep 2025 10:45 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/174909 |
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