Summary of Gazefusion: Saliency-guided Image Generation, by Yunxiang Zhang et al.
GazeFusion: Saliency-Guided Image Generation
by Yunxiang Zhang, Nan Wu, Connor Z. Lin, Gordon Wetzstein, Qi Sun
First submitted to arxiv on: 16 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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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 generating images using diffusion models that can control where viewers pay attention. Currently, existing methods allow users to specify the desired spatial layouts but cannot predict or control viewer attention due to the complexity of human vision. The proposed saliency-guided framework incorporates data priors of human visual attention mechanisms into the generation process, allowing for images that attract viewers’ attention towards specific regions. The approach is evaluated through an eye-tracked user study and a large-scale model-based saliency analysis, demonstrating alignment with desired attention distributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates better pictures using computers just by telling them what to show. Right now, the pictures are good but we can’t control where people look at them. The researchers developed a new way to make pictures that take into account how people’s eyes move when they look at things. They tested this approach and found that it works well. This could be used for many things like designing websites or creating games. |
Keywords
» Artificial intelligence » Alignment » Attention » Diffusion
