Summary of D2styler: Advancing Arbitrary Style Transfer with Discrete Diffusion Methods, by Onkar Susladkar et al.
D2Styler: Advancing Arbitrary Style Transfer with Discrete Diffusion Methods
by Onkar Susladkar, Gayatri Deshmukh, Sparsh Mittal, Parth Shastri
First submitted to arxiv on: 7 Aug 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 paper proposes a novel framework called D^2Styler for arbitrary style transfer (AST) in image processing. AST is challenging due to mode-collapse, over-stylization, or under-stylization caused by disparities between style and content images. D^2Styler leverages VQ-GANs’ discrete representational capabilities and discrete diffusion’s advantages, including stable training and avoidance of mode collapse. The method uses Adaptive Instance Normalization (AdaIN) features as a context guide for the reverse diffusion process. This allows features to be moved from style images to content images without bias. Experimental results demonstrate that D^2Styler produces high-quality style-transferred images, outperforming twelve existing methods on nearly all metrics. The paper showcases the efficacy of the technique using WikiArt dataset’s style images and COCO dataset’s content images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to change the style of an image while keeping its meaning intact. This is a challenging task in computer vision, but researchers have developed a new way to do it called D^2Styler. The technique uses special algorithms and mathematical tricks to make sure the resulting image looks good and captures the right meaning. In tests, this method worked better than other approaches on many measures. The research could lead to new ways of creating artistic images or even fake pictures that are hard to tell apart from real ones. |
Keywords
» Artificial intelligence » Diffusion » Style transfer
