SEAN: Image Synthesis with Semantic Region-Adaptive Normalization
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.
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Peihao Zhu (add twitter)
Rameen Abdal (add twitter)
Yipeng Qin (add twitter)
Peter Wonka (edit)
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Machinelearn7: RT @amArunava: SEAN: Image Synthesis with Semantic Region-Adaptive Normalization https://t.co/0vqGkR3RMv https://t.co/JPc6SyvQS0 #Machine…
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arxiv_pop: 2019/11/28 投稿 1位 CV(Computer Vision and Pattern Recognition) SEAN: Image Synthesis with Semantic Region-Adaptive Normalization https://t.co/rYJKx5WgJE 6 Tweets 41 Retweets 145 Favorites
k_takuya_2001: RT @roadrunning01: SEAN: Image Synthesis with Semantic Region-Adaptive Normalization pdf: https://t.co/7MnjKPyPh8 abs: https://t.co/qw4s994…
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arxiv_csgr: SEAN: Image Synthesis with Semantic Region-Adaptive Normalization https://t.co/NSy6JQg0sU
udoooom: 左側の図,既視感しかない SEAN: Image Synthesis with Semantic Region-Adaptive Normalization Peihao Zhu, Rameen Abdal, Yipeng Qin, Peter Wonka The interactive demo is available at this https URL https://t.co/6VpyCaZCuj https://t.co/12UyZxpipb
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