Summary of Svgbuilder: Component-based Colored Svg Generation with Text-guided Autoregressive Transformers, by Zehao Chen et al.
SVGBuilder: Component-Based Colored SVG Generation with Text-Guided Autoregressive Transformers
by Zehao Chen, Rong Pan
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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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 abstract proposes a new approach to generating Scalable Vector Graphics (SVGs) using a component-based, autoregressive model called SVGBuilder. This model reduces computational overhead and improves efficiency compared to traditional methods, allowing for the generation of high-quality colored SVGs up to 604 times faster than optimization-based approaches. The paper also introduces ColorSVG-100K, a large-scale dataset of colored SVGs that fills a gap in color information for SVG generation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SVGBuilder is a new way to create Scalable Vector Graphics (SVGs) using words as input. It’s much faster and better than old methods and can make complex graphics. The team also made a big collection of colorful SVGs called ColorSVG-100K that will help other researchers make better graphics. |
Keywords
» Artificial intelligence » Autoregressive » Optimization
