AutoFigure: Generating and Refining Publication-Ready Scientific Illustrations
High-quality scientific illustrations are crucial for effectively communicating complex scientific and technical concepts, yet their manual creation remains a well-recognized bottleneck in both academia and industry. We present FigureBench, the first large-scale benchmark for generating scientific illustrations from long-form scientific texts. It contains 3,300 high-quality scientific text-figure pairs, covering diverse text-to-illustration tasks from scientific papers, surveys, blogs, and textbooks. Moreover, we propose AutoFigure, the first agentic framework that automatically generates high-quality scientific illustrations based on long-form scientific text. Specifically, before rendering the final result, AutoFigure engages in extensive thinking, recombination, and validation to produce a layout that is both structurally sound and aesthetically refined, outputting a scientific illustration that achieves both structural completeness and aesthetic appeal. Leveraging the high-quality data from FigureBench, we conduct extensive experiments to test the performance of AutoFigure against various baseline methods. The results demonstrate that AutoFigure consistently surpasses all baseline methods, producing publication-ready scientific illustrations. The code, dataset and huggingface space are released in https://github.com/ResearAI/AutoFigure.
69838eece34659da7e1f4c3e
Minjun Zhu
69838eece34659da7e1f4c3f
Zhen Lin
claimed_verified
2026-03-11T16:31:56.164Z
664c3a9311838470cca8f679
/avatars/d939b877a47158cecf3da3415cc726ac.svg
ZhenLin
chuan42
user
chuan42
69838eece34659da7e1f4c40
Yixuan Weng
69838eece34659da7e1f4c41
Panzhong Lu
69838eece34659da7e1f4c42
Qiujie Xie
69838eece34659da7e1f4c43
Yifan Wei
69838eece34659da7e1f4c44
Sifan Liu
69838eece34659da7e1f4c45
Qiyao Sun
69838eece34659da7e1f4c46
Yue Zhang
AutoFigure: Generating and Refining Publication-Ready Scientific Illustrations
High-quality scientific illustrations are crucial for effectively communicating complex scientific and technical concepts, yet their manual creation remains a well-recognized bottleneck in both academia and industry. We present FigureBench, the first large-scale benchmark for generating scientific illustrations from long-form scientific texts. It contains 3,300 high-quality scientific text-figure pairs, covering diverse text-to-illustration tasks from scientific papers, surveys, blogs, and textbooks. Moreover, we propose AutoFigure, the first agentic framework that automatically generates high-quality scientific illustrations based on long-form scientific text. Specifically, before rendering the final result, AutoFigure engages in extensive thinking, recombination, and validation to produce a layout that is both structurally sound and aesthetically refined, outputting a scientific illustration that achieves both structural completeness and aesthetic appeal. Leveraging the high-quality data from FigureBench, we conduct extensive experiments to test the performance of AutoFigure against various baseline methods. The results demonstrate that AutoFigure consistently surpasses all baseline methods, producing publication-ready scientific illustrations. The code, dataset and huggingface space are released in https://github.com/ResearAI/AutoFigure.
FigureBench presents the first large-scale benchmark for generating scientific illustrations from long-form scientific texts, while AutoFigure introduces an agentic framework that produces publication-ready illustrations through extensive thinking, recombination, and validation processes.
scientific illustrations long-form scientific text FigureBench AutoFigure agentic framework text-to-illustration tasks scientific papers surveys textbooks structural completeness aesthetic appeal publication-ready