Chart2SVG：从栅格图表图像生成可编辑的SVG

中文摘要 AI 辅助

我们提出Chart2SVG，这是一种多模态大语言模型，可将静态栅格图表转换为结构有序、语义丰富且支持程序化编辑的SVG。通过将图表特定语义令牌融入视觉-语言模型，Chart2SVG能同时捕捉几何基元及其功能角色。为支持鲁棒的结构恢复，我们引入Beagle+，这是一个包含3.3万个标准化且结构提炼的图表样本的数据集。我们的方法结合了专门的训练目标与渲染感知的后训练阶段，生成的SVG在视觉上准确且结构一致。为便于更高层级的操作，我们构建了Chart Structure Graph（CSG），它揭示了视觉依赖关系，支持交互式探索、图表再利用和布局复用等任务。实验表明，Chart2SVG在重建保真度和下游编辑实用性方面显著优于基线模型，推动了智能交互式可视化工具的发展。

英文摘要

We present Chart2SVG, a multimodal large language model that converts static raster charts into structurally organized, semantically enriched SVGs that support programmatic editing. By incorporating chart-specific semantic tokens into a vision-language model, Chart2SVG captures both geometric primitives and their functional roles. To support robust structural recovery, we introduce Beagle+, a dataset of 33K canonicalized and structurally distilled chart samples. Our approach combines specialized training objectives with a rendering-aware post-training phase, producing SVGs that are both visually accurate and structurally consistent. To facilitate higher-level manipulations, we construct a Chart Structure Graph (CSG) that exposes visual dependencies, enabling tasks such as interactive exploration, chart repurposing, and layout reuse. Experiments show that Chart2SVG substantially outperforms baselines in reconstruction fidelity and downstream editing utility, advancing the development of intelligent and interactive visualization tools.

发表机构

• Renmin University of China（中国人民大学）
• State Key Lab of CAD&CG, Zhejiang University（浙江大学CAD&CG国家重点实验室）
• Shandong University（山东大学）
• Microsoft Research（微软研究院）
• Qwen Large Model Application Team, Alibaba（阿里巴巴通义大模型应用团队）
• University of Technology Sydney（悉尼科技大学）

机构由 AI 辅助整理，请以论文原文为准。