Zhu, Peihao, Abdal, Rameen, Qin, Yipeng  ORCID: https://orcid.org/0000-0002-1551-9126 and Wonka, Peter
2020.
SEAN: image synthesis with semantic region-adaptive normalization.
Presented at: Conference on Computer Vision and Pattern Recognition (CVPR 2020),
Seattle, Washington, USA,
14-19 June 2020.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
IEEE,
pp. 5103-5112.
10.1109/CVPR42600.2020.00515
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Abstract
We propose semantic region-adaptive normalization (SEAN),asimplebuteffectivebuildingblockforGenerative Adversarial Networks conditioned on segmentation masks that describe the semantic regions in the desired output image. Using SEAN normalization, we can build a network architecture that can control the style of each semantic region individually, e.g., we can specify one style reference image per region. SEAN is better suited to encode, transfer, and synthesize style than the best previous method in terms of reconstruction quality, variability, and visual quality. We evaluateSEANonmultipledatasetsandreportbetterquan titative metrics (e.g. FID, PSNR) than the current state of the art. SEAN also pushes the frontier of interactive image editing. We can interactively edit images by changing segmentation masks or the style for any given region. We can also interpolate styles from two reference images per region. Code: https://github.com/ZPdesu/SEAN.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Published Online |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | IEEE |
| ISBN: | 9781728171692 |
| ISSN: | 1063-6919 |
| Date of First Compliant Deposit: | 5 May 2020 |
| Date of Acceptance: | 27 February 2020 |
| Last Modified: | 24 Sep 2025 10:30 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/131426 |
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