Papers › Cross-domain Correspondence Learning for Exemplar-based Image Translation
Cross-domain Correspondence Learning for Exemplar-based Image Translation
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image-to-Image Translation | ADE20K Labels-to-Photos | CoCosNet | FID | 26.4 | #14 of 16 | Archive leaderboard | report |
| Image-to-Image Translation | ADE20K-Outdoor Labels-to-Photos | CoCosNet | FID | 42.4 | #7 of 7 | Archive leaderboard | report |
| Image-to-Image Translation | CelebA-HQ | CoCosNet | FID | 14.3 | #2 of 6 | Archive leaderboard | report |
| Image-to-Image Translation | Deep-Fashion | CoCosNet | FID | 14.4 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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