SEAN: Image Synthesis with Semantic Region-Adaptive Normalization

CVPR 2020 Peihao ZhuRameen AbdalYipeng QinPeter Wonka

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... (read more)

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