Summary of Eye-for-an-eye: Appearance Transfer with Semantic Correspondence in Diffusion Models, by Sooyeon Go and Kyungmook Choi and Minjung Shin and Youngjung Uh
Eye-for-an-eye: Appearance Transfer with Semantic Correspondence in Diffusion Models
by Sooyeon Go, Kyungmook Choi, Minjung Shin, Youngjung Uh
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to achieve appearance transfer in text-to-image diffusion models. Specifically, it enables the production of results that share the same structure as a target image but adopt the colors from a reference image, following semantic correspondences between the result and reference images. For instance, the wing area of the result takes its color from the reference’s wing, rather than its head. The proposed method addresses limitations in existing approaches that rely on self-attention layers and typically produce defective results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making pictures look like they’re painted with colors from another picture! Imagine you want a cat to have the same body shape as one picture, but the fur color of another. Existing methods try to make this happen by comparing parts of the two pictures, but it doesn’t always work well. This new method finds the matching parts between the two pictures and rearranges the colors accordingly. It works really well in many cases, even when the two pictures aren’t perfectly aligned. |
Keywords
» Artificial intelligence » Diffusion » Self attention
