Summary of Generating Non-stationary Textures Using Self-rectification, by Yang Zhou et al.
Generating Non-Stationary Textures using Self-Rectification
by Yang Zhou, Rongjun Xiao, Dani Lischinski, Daniel Cohen-Or, Hui Huang
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG)
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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 two-step approach to example-based non-stationary texture synthesis. The method first allows users to modify a reference texture using standard image editing tools, creating an initial rough target for the synthesis. Then, the “self-rectification” process refines this target into a coherent and seamless texture that retains the distinct visual characteristics of the original exemplar. The approach leverages pre-trained diffusion networks and self-attention mechanisms to align the synthesized texture with the reference. Experimental results demonstrate significant advancements in texture synthesis compared to existing state-of-the-art techniques, particularly for non-stationary textures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to create new textures that look like real-world materials. It does this by letting users start with a texture and then making small changes to get the desired result. The program uses special computer networks and attention mechanisms to make sure the new texture looks good and is similar to the original one. This helps when trying to create textures that change or move in interesting ways, like ripples on water or patterns on fabric. |
Keywords
* Artificial intelligence * Attention * Diffusion * Self attention
