Chart2SVG: Editable SVG Generation from Raster Chart Images

2026-08-26

AI Summary

本文提出Chart2SVG模型，通过结合图表特异性语义令牌和专门训练目标，将光栅图表转换为可编辑的SVG格式，解决了图表结构化和语义丰富的问题。

Abstract

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.

Problem

• Chart2SVG
• raster charts
• SVG generation
• programmatic editing
• semantic enrichment

Innovation

• multimodal large language model
• semantic tokens
• Beagle+
• rendering-aware post-training
• Chart Structure Graph (CSG)