@inproceedings{lin-etal-2026-autofigure,
title = "{A}uto{F}igure-Edit: Generating Editable Scientific Illustrations via Reference-Guided Styling",
author = "Lin, Zhen and
Xie, Qiujie and
Zhu, Minjun and
Li, Shichen and
Sun, QiYao and
Gu, Enhao and
Ding, Yiran and
Sun, Ke and
Guo, Fang and
Lu, Panzhong and
Ning, Zhiyuan and
Weng, Yixuan and
Zhang, Yue",
editor = "Durrett, Greg and
Jian, Ping",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-demo.6/",
pages = "57--67",
ISBN = "979-8-89176-392-0",
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, the full codebase at https://github.com/ResearAI/AutoFigure-Edit and provide a live demo for easy access and interactive use at https://autofigure.cc/."
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%0 Conference Proceedings
%T AutoFigure-Edit: Generating Editable Scientific Illustrations via Reference-Guided Styling
%A Lin, Zhen
%A Xie, Qiujie
%A Zhu, Minjun
%A Li, Shichen
%A Sun, QiYao
%A Gu, Enhao
%A Ding, Yiran
%A Sun, Ke
%A Guo, Fang
%A Lu, Panzhong
%A Ning, Zhiyuan
%A Weng, Yixuan
%A Zhang, Yue
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F lin-etal-2026-autofigure
%X 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, the full codebase at https://github.com/ResearAI/AutoFigure-Edit and provide a live demo for easy access and interactive use at https://autofigure.cc/.
%U https://aclanthology.org/2026.acl-demo.6/
%P 57-67
Markdown (Informal)
[AutoFigure-Edit: Generating Editable Scientific Illustrations via Reference-Guided Styling](https://aclanthology.org/2026.acl-demo.6/) (Lin et al., ACL 2026)
ACL
- Zhen Lin, Qiujie Xie, Minjun Zhu, Shichen Li, QiYao Sun, Enhao Gu, Yiran Ding, Ke Sun, Fang Guo, Panzhong Lu, Zhiyuan Ning, Yixuan Weng, and Yue Zhang. 2026. AutoFigure-Edit: Generating Editable Scientific Illustrations via Reference-Guided Styling. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 57–67, San Diego, California, United States. Association for Computational Linguistics.