Back2Struct is a system that converts structured images—such as diagrams, charts, and flowcharts—into editable vector graphics code (SVG/XML). By predicting semantically rich, object-level SVG code rather than low-level pixel vectorization, it allows the generated graphics to be imported into tools like PowerPoint for easy editing, restyling, and reuse. The model is trained with supervised fine‑tuning and reward‑based learning that enforces syntactic validity, concise length, and visual fidelity to the input, leading to higher accuracy, editability, and user alignment compared to baselines.
By Pengyu Yan, Yixin Wu, Yunjie Tian, David Doermann
Chart2SVG is a multimodal large language model that transforms static raster chart images into editable SVGs enriched with semantic structure. By embedding chart‑specific semantic tokens into a vision‑language framework and training on the Beagle+ dataset of 33K distilled chart samples, the model captures both geometric primitives and their functional roles. The resulting SVGs are visually accurate and structurally consistent, and the accompanying Chart Structure Graph (CSG) exposes visual dependencies for interactive exploration, chart repurposing, and layout reuse.
By Jinning Cui, Lu Chen, Haoyan Shi, Yue He, Chenglong Wang, Mengyu Zhou, Weidong Huang, Yunhai Wang
arXiv:2603. 24575v2 Announce Type: replace-cross Abstract: Scalable Vector Graphics (SVG) are essential for technical illustration and digital design, offering resolution independence and semantic editability.
By Qijia He, Xunmei Liu, Hammaad Memon, Ziang Li, Zixian Ma, Jaemin Cho, Zhongzheng Ren, Daniel S Weld, Ranjay Krishna
arXiv:2607. 28073v1 Announce Type: new Abstract: In demanding professional environments and meeting review scenarios, lengthy text often imposes a high cognitive load.
By Yiming Xu, Jihua Kang, Chunsai Du, Qifan Zhang, Wangqiu Zhou, Yiting Wu, Tianqi Li, Qi Song
arXiv:2509. 05208v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise visual content remains underexplored.
By Yamei Chen, Haoquan Zhang, Yangyi Huang, Zeju Qiu, Kaipeng Zhang, Yandong Wen, Weiyang Liu
arXiv:2603.29852v2 Announce Type: replace-cross
Abstract: We introduce VectorGym, a comprehensive benchmark suite for Scalable Vector Graphics (SVG) that spans generation from text and sketches, comp...
By Joan Rodriguez, Haotian Zhang, Abhay Puri, Haoran Dai, Tianyang Zhang, Meng Lin, Rishav Pramanik, Xiaoqing Xie, Marco Terral Rodriguez, Darsh Kaushik, Aly Shariff, Perouz Taslakian, Spandana Gella, Sai Rajeswar, David Vazquez, Christopher Pal, Marco Pedersoli
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
Scientific methodology figures are essential for communicating complex methods clearly, yet creating them remains labor-intensive and typically requires multiple rounds of refinement. Recent image-gen...
arXiv:2609.13294v1 Announce Type: new
Abstract: Converting scientific graphics into editable representations remains a challenging problem for image-to-code generation because of their heterogeneous...
By Jiahao Tang, Yiren Song, Alex Jinpeng Wang
arXiv:2608. 12611v1 Announce Type: cross Abstract: Existing screenshot-to-code systems face a trade-off between flexibility and controllability.
By Houston H. Zhang, Tao Zhang, Li Gu, Linfeng Ye, Yuanhao Yu, Xinxin Zuo, Yang Wang, Zhixiang Chi
arXiv:2607. 15272v1 Announce Type: cross Abstract: Editing the figures in a research paper is a routine and time-consuming part of everyday research practice: authors relabel components, rearrange panels, and restyle visuals as they revise their manuscripts.
By Yasheng Sun, Zezi Zeng, Yifan Yang, Chong Luo, Wenyi Wang, Ziwei Liu, J\"urgen Schmidhuber
arXiv:2606. 10334v1 Announce Type: new Abstract: Code-generating large language models (LLMs) increasingly produce visual artifacts such as charts, web pages, and slides by writing programs that are executed by non-differentiable renderers, committing to code before observing the render.
By Haoyu Dong