StyleGAN-∞: extending StyleGAN to arbitrary-ratio translation with StyleBook

Dai, Yihua, Xiang, Tianyi, Deng, Bailin ORCID: https://orcid.org/0000-0002-0158-7670, Du, Yong, Cai, Hongmin, Qin, Jing and He, Shengfeng 2024. StyleGAN-∞: extending StyleGAN to arbitrary-ratio translation with StyleBook. IEEE Transactions on Visualization and Computer Graphics 10.1109/tvcg.2024.3522565

Preview	PDF - Accepted Post-Print Version Download (60MB) | Preview
Preview	PDF - Supplemental Material Download (58MB) | Preview

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:	Published Online
Status:	In Press
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:	16 Jan 2025 16:30
URI:	https://orca.cardiff.ac.uk/id/eprint/174909

Actions (repository staff only)

Edit Item