arXiv AI
VectorGym: A Multi-Task Benchmark for SVG Code Generation, Sketching and Editing
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arXiv AI
Aug 18
VFIG: Vectorizing Complex Figures in SVG with Vision-Language Models
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.
arXiv Machine Learning
Aug 10
Symbolic Graphics Programming with Large Language Models
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.
arXiv Machine Learning
Jul 31
GVR-Coder: A Visual-Feedback Framework for Structured SVG Generation in Complex Document and Meeting Scenarios
arXiv:2607. 28073v1 Announce Type: new Abstract: In demanding professional environments and meeting review scenarios, lengthy text often imposes a high cognitive load.
arXiv Computer Vision
Aug 25
VinciCoder: Unifying Multimodal Code Generation via Coarse-to-fine Visual Reinforcement Learning
VinciCoder is a unified framework for multimodal code generation that addresses the limitations of single-task models by training on a large-scale curated corpus of 1.3 M direct generation pairs and 300 k visual‑refinement tasks. It introduces a coarse‑to‑fine Visual Reinforcement Learning (ViRL) approach that uses visual similarity across multi‑scale patches to provide an implementation‑agnostic reward, improving alignment between rendered outputs and input visuals. Experiments on diverse benchmarks show VinciCoder outperforms existing methods, and ablation studies confirm the effectiveness of ViRL.
arXiv Machine Learning
6d ago
Chart2SVG: Editable SVG Generation from Raster Chart Images
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.
Hugging Face Trending Papers
Aug 11
VisEditBench: Can Vision-Language Models Edit Visualization Code from Multimodal Feedback?
Vision-language models (VLMs) have shown strong capabilities in generating visualization code from textual or visual specifications. However, real-world visualization authoring is inherently iterative: users frequently revise existing visualizations to repair flawed charts or adapt them to desired styles.