Chart2SVG: ラスターチャート画像からの編集可能なSVG生成
Chart2SVG: Editable SVG Generation from Raster Chart Images
ラスターチャート画像を、構造化され意味的に豊かなSVGに変換するマルチモーダル大規模言語モデルを提案。チャート固有のセマンティックトークンと専用データセットを用いて、視覚的精度と構造的一貫性を両立した編集可能なSVGを生成する。
著者: Jinning Cui, Lu Chen, Haoyan Shi, Yue He, Chenglong Wang, Mengyu Zhou, Weidong Huang, Yunhai Wang
分類: cs.LG
原文アブストラクト
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.