CHARTSTYLE-100K: A Large-Scale Dataset for Structured Visualization Style Transfer

Given an input image, style transfer aims to recast it into the style of a reference image while preserving its content. While this problem is well addressed for natural images, we find that structured images such as charts, diagrams and tables demand capabilities fundamentally distinct from natural image stylization, i.e. visual elements encode data through geometry (bar heights, arc angles, point positions), and dense text must be preserved exactly. We term this structured style transfer as changing appearance while keeping strict element-level fidelity. Even frontier models like GPT-Image-1.5 and Nano-Banana-Pro often struggle here, producing inconsistent transfer, structural distortion, and content leakage. The obvious fix, training on data that restyles a content chart toward a reference, inherits these failures: it distorts the content and leaks the reference into the target. We introduce ChartForge, a data pipeline that instead builds triplets in reverse: starting from a style image, it produces a target of similar style but different content, then a matching content chart. Because the content is derived from the target, the two stay structurally are disentangled from the start, avoiding forwardpipeline leakage. With style-space resampling and multi-dimensional filtering, ChartForge yields ChartStyle-100K, over 100K triplets across 36 chart types and 3 visualization families. For evaluation, we further build ChartStyle-Bench, a benchmark of 300 content-style pairs with 6 metrics. Using ChartStyle-100K, Training ReChart by progressively fine-tuning Qwen-Image-Edit on ChartStyle-100K achieves the best overall performance across all baselines, including GPT-Image-1.5 and Nano-Banana-Pro.