Summary of Pattern Analogies: Learning to Perform Programmatic Image Edits by Analogy, By Aditya Ganeshan et al.
Pattern Analogies: Learning to Perform Programmatic Image Edits by Analogy
by Aditya Ganeshan, Thibault Groueix, Paul Guerrero, Radomír Měch, Matthew Fisher, Daniel Ritchie
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Human-Computer Interaction (cs.HC)
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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 novel approach enables programmatic edits on pattern images by introducing a pair of simple patterns as an analogy and leveraging a learning-based generative model to execute these edits. The SplitWeave domain-specific language, combined with synthetic pattern analogies, creates a large high-quality training dataset. TriFuser, a Latent Diffusion Model (LDM), overcomes critical issues in deploying LDMs for this task. Experimental results demonstrate faithful performance and generalization to related patterns beyond the training distribution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to edit a pattern, like a repeating design on fabric. This can be tricky because you want to make specific changes to the underlying “program” that generates the pattern. Current methods struggle with complex images and produce messy results. A team of researchers has developed a new approach to edit patterns more easily. They use simple examples of what they want to achieve, called “pattern analogies,” and a special model to apply these edits. To make this work, they created a new language (SplitWeave) and a system for generating lots of similar pattern examples. They also designed a Latent Diffusion Model (LDM) to overcome challenges in using this approach. The results show that their method can accurately edit patterns while also learning to apply these changes to related designs. |
Keywords
» Artificial intelligence » Diffusion model » Generalization » Generative model
