Cross-domain Correspondence Learning for Exemplar-based Image Translation

CVPR 2020  ·  Pan Zhang, Bo Zhang, Dong Chen, Lu Yuan, Fang Wen ·

We present a general framework for exemplar-based image translation, which synthesizes a photo-realistic image from the input in a distinct domain (e.g., semantic segmentation mask, or edge map, or pose keypoints), given an exemplar image. The output has the style (e.g., color, texture) in consistency with the semantically corresponding objects in the exemplar. We propose to jointly learn the crossdomain correspondence and the image translation, where both tasks facilitate each other and thus can be learned with weak supervision. The images from distinct domains are first aligned to an intermediate domain where dense correspondence is established. Then, the network synthesizes images based on the appearance of semantically corresponding patches in the exemplar. We demonstrate the effectiveness of our approach in several image translation tasks. Our method is superior to state-of-the-art methods in terms of image quality significantly, with the image style faithful to the exemplar with semantic consistency. Moreover, we show the utility of our method for several applications

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Image-to-Image Translation ADE20K Labels-to-Photos CoCosNet FID 26.4 # 1
Image-to-Image Translation ADE20K-Outdoor Labels-to-Photos CoCosNet FID 42.4 # 1
Image-to-Image Translation CelebA-HQ CoCosNet FID 14.3 # 2
Image-to-Image Translation Deep-Fashion CoCosNet FID 14.4 # 2

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