Summary of Ebdm: Exemplar-guided Image Translation with Brownian-bridge Diffusion Models, by Eungbean Lee et al.
EBDM: Exemplar-guided Image Translation with Brownian-bridge Diffusion Models
by Eungbean Lee, Somi Jeong, Kwanghoon Sohn
First submitted to arxiv on: 13 Oct 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 The proposed Exemplar-guided Image Translation with Brownian-Bridge Diffusion Models (EBDM) methodology enhances user control over style manipulation by synthesizing photo-realistic images conforming to both structural control and style exemplars. By formulating the task as a stochastic Brownian bridge process, EBDM efficiently translates images from structure control while conditioned solely on the given exemplar image. The novel approach comprises three pivotal components: Global Encoder, Exemplar Network, and Exemplar Attention Module, which incorporate global and detailed texture information from exemplar images. This leads to more robust training and inference processes, outperforming competing approaches in comprehensive benchmark evaluations and visual results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Exemplar-guided image translation helps create realistic pictures that match both shape and style examples. Before, this was done by finding lots of matching points between different types of images, but this took up a lot of memory and computer power. This made it hard to use these methods in many situations. The new method, called Exemplar-guided Image Translation with Brownian-Bridge Diffusion Models (EBDM), uses a special process that starts at a fixed point and moves towards the desired image while being guided by an example picture. To make this process work well, the researchers developed three important parts: Global Encoder, Exemplar Network, and Exemplar Attention Module. These parts help capture details from the example image. The new method is better than old ones because it’s more efficient and can be used in many situations. |
Keywords
» Artificial intelligence » Attention » Encoder » Inference » Translation
