arXiv Machine Learning By Takuro Kawada, Shunsuke Kitada, Hitoshi Iyatomi

GenGA: Editable and Data-Grounded Graphical Abstract Generation for Academic Papers

Read the original on arXiv Machine Learning →

arXiv:2608. 05478v1 Announce Type: cross Abstract: Graphical Abstracts (GAs) visually summarize the key findings of academic papers, playing a crucial role in facilitating the understanding of research content.

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arXiv AI
Sep 2

Figures as Programs: Recursive Generation of Editable Scientific Figures

The paper introduces “FigTree”, a multi-agent system that automatically converts a scientific paper into a structured vector figure by recursively constructing SVG programs. It decomposes figures into hierarchical regions, generates each region as a short SVG program, and assembles them, using a render‑critic refinement loop to trace and repair visual defects. Evaluations show that “FigTree” produces high‑quality figures and allows more effective editing than raster‑based methods.

By Yepeng Liu, Dasen Dai, Chengzhi Liu, Yiren Song, Hai Ci, Yu Zhang, Qi Zhang, Mike Zheng Shou, Xin Eric Wang, Yuheng Bu
arXiv Computer Vision
Sep 1

DisciplineGen-1M: A Large-Scale Dataset for Multidisciplinary Visual Generation and Editing

arXiv:2607.02290v2 Announce Type: replace Abstract: Recent image generation and editing models can produce visually appealing natural images, yet they remain unreliable when the target image is a kno...

By Zhaokai Wang, Mingxin Liu, Zirun Zhu, Ziqian Fan, Yiguo He, Mohan Zhang, Leyao Gu, Yan Li, Xiangyu Zhao, Ning Liao, Shaofeng Zhang, Xuanhe Zhou, Zhihang Zhong, Xue Yang