AutoFigure-Edit: Generating Editable Scientific Illustration
Zhen Lin, Qiujie Xie, Minjun Zhu, Shichen Li, Qiyao Sun, Enhao Gu, Yiran Ding, Ke Sun, Fang Guo, Panzhong Lu, Zhiyuan Ning, Yixuan Weng, Yue Zhang

TL;DR
AutoFigure-Edit is an end-to-end system that generates fully editable, style-adaptable scientific illustrations from text, combining long-context understanding, reference-guided styling, and native SVG editing for efficient creation.
Contribution
It introduces a novel system that produces editable scientific illustrations from text with flexible style control using reference images.
Findings
Enables efficient creation of high-quality scientific illustrations.
Supports flexible style adaptation through user-provided references.
Provides an interactive platform and open-source code for the community.
Abstract
High-quality scientific illustrations are essential for communicating complex scientific and technical concepts, yet existing automated systems remain limited in editability, stylistic controllability, and efficiency. We present AutoFigure-Edit, an end-to-end system that generates fully editable scientific illustrations from long-form scientific text while enabling flexible style adaptation through user-provided reference images. By combining long-context understanding, reference-guided styling, and native SVG editing, it enables efficient creation and refinement of high-quality scientific illustrations. To facilitate further progress in this field, we release the video at https://youtu.be/10IH8SyJjAQ, full codebase at https://github.com/ResearAI/AutoFigure-Edit and provide a website for easy access and interactive use at https://deepscientist.cc/.
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Video Analysis and Summarization · Digital Humanities and Scholarship
