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）是一种基于XML的通用图形格式，具备分辨率无关性和可扩展性。与光栅图像不同，SVG采用几何图形构建，并支持通过CSS和JavaScript实现交互、动画和操控。当前SVG生成方法面临计算成本高和复杂度大的挑战。相比之下，人类设计师采用基于组件的工具高效创建SVG。受此启发，SVGBuilder引入了一种基于组件的自回归模型，能够从文本输入生成高质量的彩色SVG。相较于传统方法，该模型显著降低了计算开销并提升了效率——其生成速度比基于优化的方法快604倍。为弥补现有SVG数据集的不足并支持本研究，我们推出了ColorSVG-100K，这是首个大规模彩色SVG数据集，包含10万个图形。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。与现有最优模型的对比评估表明，SVGBuilder在实际应用中展现出卓越性能，在复杂SVG图形生成的效率与质量方面优势显著。