用文本生成彩色矢量图，速度比传统方法快604倍
SVGBuilder: Component-Based Colored SVG Generation with Text-Guided Autoregressive Transformers
- 基于组件的自回归模型，分步生成矢量图形
- 生成速度比优化方法快604倍，支持复杂彩色图形
- 适合需要高效生成高质量矢量图的设计师或开发者
可扩展矢量图形（SVG）是基于XML的通用图形格式，具备分辨率无关性和可伸缩性。与位图不同，SVG使用几何形状，支持通过CSS和JavaScript实现交互、动画和操作。当前的SVG生成方法存在计算成本高、流程复杂的问题。受人类设计师使用组件化工具启发，SVGBuilder提出一种基于组件的自回归模型，仅需文本输入即可生成高质量彩色SVG。该模型生成速度较传统优化方法提升604倍。为弥补现有数据集在颜色信息上的不足，研究构建了首个大规模彩色SVG数据集ColorSVG-100K，包含10万张彩色图形，显著提升模型训练多样性。在多项基准测试中，SVGBuilder在实际应用中表现优于现有最先进模型，验证了其在效率与质量上的优势。
原文摘要 · Abstract (English)
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.
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