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 large-scale dataset of colored SVGs, comprising 100,000 graphics. This dataset fills the gap in color information for SVG generation models and enhances diversity in model training. Evaluation against state-of-the-art models demonstrates SVGBuilder's superior performance in practical applications, highlighting its efficiency and quality in generating complex SVG graphics.

翻译：可缩放矢量图形（SVG）是基于XML的多功能图形核心格式，具有分辨率无关性和可缩放性。与栅格图像不同，SVG采用几何形状构建，并支持通过CSS和JavaScript实现交互性、动画及动态操控。当前SVG生成方法面临高计算成本与复杂性的挑战。相比之下，人类设计师采用基于组件的工具实现高效SVG创作。受此启发，SVGBuilder提出一种基于组件的自回归模型，能够根据文本输入生成高质量彩色SVG。与传统方法相比，该模型显著降低了计算开销并提升了生成效率。实验表明，本模型生成速度比基于优化的方法快604倍。为弥补现有SVG数据集的不足并支撑研究，我们提出了首个大规模彩色SVG数据集ColorSVG-100K，包含10万个图形样本。该数据集填补了SVG生成模型在色彩信息方面的空白，并增强了模型训练的多样性。与前沿模型的对比评估证明，SVGBuilder在实际应用中具有卓越性能，在生成复杂SVG图形方面展现出高效性与高质量特性。