ArtChart：一个用于忠实生成艺术图表并集成文本渲染的基准测试

中文摘要 AI 辅助

艺术图表能让数据令人难忘且视觉上引人入胜，但要忠实地生成它们，需要同时保留数值几何、渲染精确的图像内文本、将标签绑定到正确的标记以及保持连贯的艺术风格。当前的文本到图像和图像编辑模型在这些耦合约束上经常失败。本文介绍了ArtChart，一个用于艺术图表生成并集成文本渲染的框架，包括任务定义、基准测试和评估协议。它是首个同时解决数学上忠实的图表合成、准确的图像内文本渲染以及图表元素艺术风格化的工作。ArtChart具有一个基于无文本灰度图表布局的特定于图表的即插即用模块，通过带有OCR准确性、布局质量和美学奖励的强化学习策略优化生成，并用多专家蒸馏框架解决奖励冲突。还构建了ArtChart - Bench基准测试和ArtChart - Eval评估套件。大量实验表明ArtChart始终优于开源基线，生成的图表既美观又忠实于数学。

英文摘要

Artistic charts combine data visualization with expressive marks, textures, and typography, but they are difficult for image generators: an output is useful only when its stylization preserves chart geometry, exact in-image text, and the semantic binding between labels and marks. We introduce ArtChart, a framework for faithful artistic chart generation with integrated text rendering. Given a structured chart specification and an artistic prompt, ArtChart first renders a text-free grayscale layout that encodes the target chart geometry, then trains a chart-specific control module to preserve mathematical structure. To address the remaining text and layout errors, we further refine the generation policy through GRPO-based reinforcement learning with OCR-based text rewards, VLM-based layout rewards, and aesthetic rewards. A multi-expert distillation stage reconciles these objectives by distilling single-reward experts into one balanced generation policy. We also construct ArtChart-Bench, a bilingual 2K-prompt benchmark covering four chart types, controlled value distributions, diverse label/value formats, and 15 artistic styles, together with ArtChart-Eval, a six-axis evaluation protocol measuring mathematical logic, text accuracy, text layout, aesthetics, instruction following, and readability. Experiments on ArtChart-Bench show that ArtChart consistently outperforms prompt-only, image-editing, and generic ControlNet baselines, with the largest gains on mathematical fidelity and label-layout binding while maintaining competitive visual quality. These results suggest that artistic chart generation should be evaluated as reliable visual communication rather than as generic stylized image synthesis.

发表机构

• Ant Group（蚂蚁集团）

机构由 AI 辅助整理，请以论文原文为准。