Summary of Textile: a Differentiable Metric For Texture Tileability, by Carlos Rodriguez-pardo et al.
TexTile: A Differentiable Metric for Texture Tileability
by Carlos Rodriguez-Pardo, Dan Casas, Elena Garces, Jorge Lopez-Moreno
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces TexTile, a novel differentiable metric to quantify the degree to which a texture image can be concatenated with itself without introducing repeating artifacts. The existing methods for tileable texture synthesis focus on general texture quality but lack explicit analysis of intrinsic repeatability properties. TexTile evaluates tileable properties, enabling more informed synthesis and analysis of tileable textures. It’s formulated as a binary classifier built from a large dataset of textures with different styles, semantics, regularities, and human annotations. The method incorporates architectural modifications to baseline pre-train image classifiers to overcome shortcomings in measuring tileability, along with custom data augmentation and training regimes aimed at increasing robustness and accuracy. TexTile can be plugged into state-of-the-art texture synthesis methods, including diffusion-based strategies, generating tileable textures while maintaining or improving overall texture quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to measure how well a texture image can be repeated without looking fake. Current methods focus on making the texture look good but don’t think about whether it can be repeated successfully. The new method, called TexTile, helps make better decisions when creating or analyzing tileable textures. It’s like training an AI model to understand what makes a texture repeatable. This tool is useful for making better-looking repeating patterns and evaluating how well different methods do at making these patterns. |
Keywords
* Artificial intelligence * Data augmentation * Diffusion * Semantics
