SEAN: Image Synthesis With Semantic Region-Adaptive Normalization

Peihao Zhu, Rameen Abdal, Yipeng Qin, Peter Wonka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

We propose semantic region-adaptive normalization (SEAN), a simple but effective building block for Generative 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 evaluate SEAN on multiple datasets and report better quantitative 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.
Original languageEnglish (US)
Title of host publication2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherIEEE
ISBN (Print)978-1-7281-7169-2
DOIs
StatePublished - 2020

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