SVGBuilder: Component-Based Colored SVG Generation with Text-Guided Autoregressive Transformers
Zehao Chen, Rong Pan

TL;DR
SVGBuilder is a novel component-based autoregressive model that efficiently generates high-quality colored SVG graphics from text, significantly reducing computational costs and outperforming existing methods in speed and quality.
Contribution
This paper introduces SVGBuilder, the first large-scale colored SVG dataset, and a new autoregressive model that improves SVG generation efficiency and quality from textual descriptions.
Findings
SVGBuilder generates SVGs up to 604 times faster than optimization-based methods.
The model outperforms state-of-the-art approaches in quality and efficiency.
The ColorSVG-100K dataset enhances diversity and color information for SVG generation.
Abstract
Scalable Vector Graphics (SVG) are essential XML-based formats for versatile graphics, offering resolution independence and scalability. Unlike raster images, SVGs use geometric shapes and support interactivity, animation, and manipulation via CSS and JavaScript. Current SVG generation methods face challenges related to high computational costs and complexity. In contrast, human designers use component-based tools for efficient SVG creation. Inspired by this, SVGBuilder introduces a component-based, autoregressive model for generating high-quality colored SVGs from textual input. It significantly reduces computational overhead and improves efficiency compared to traditional methods. Our model generates SVGs up to 604 times faster than optimization-based approaches. To address the limitations of existing SVG datasets and support our research, we introduce ColorSVG-100K, the first…
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Taxonomy
TopicsNatural Language Processing Techniques
