Vector Prism: Animating Vector Graphics by Stratifying Semantic Structure

• URL: http://arxiv.org/abs/2512.14336v1
• Date: Tue, 16 Dec 2025 12:03:46 GMT
• Title: Vector Prism: Animating Vector Graphics by Stratifying Semantic Structure
• Authors: Jooyeol Yun, Jaegul Choo,
• Abstract summary: We introduce a framework that recovers the semantic structure required for reliable SVG animation.<n>By reorganizing SVGs into semantic groups, our approach enables VLMs to produce animations with far greater coherence.
• Score: 57.89872230703339
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scalable Vector Graphics (SVG) are central to modern web design, and the demand to animate them continues to grow as web environments become increasingly dynamic. Yet automating the animation of vector graphics remains challenging for vision-language models (VLMs) despite recent progress in code generation and motion planning. VLMs routinely mis-handle SVGs, since visually coherent parts are often fragmented into low-level shapes that offer little guidance of which elements should move together. In this paper, we introduce a framework that recovers the semantic structure required for reliable SVG animation and reveals the missing layer that current VLM systems overlook. This is achieved through a statistical aggregation of multiple weak part predictions, allowing the system to stably infer semantics from noisy predictions. By reorganizing SVGs into semantic groups, our approach enables VLMs to produce animations with far greater coherence. Our experiments demonstrate substantial gains over existing approaches, suggesting that semantic recovery is the key step that unlocks robust SVG animation and supports more interpretable interactions between VLMs and vector graphics.

Related papers

• DuetSVG: Unified Multimodal SVG Generation with Internal Visual Guidance [48.98604326855894]
  We introduce DuetSVG, a unified multimodal model that jointly generates image tokens and corresponding SVG tokens in an end-to-end manner.<n>At inference, we apply a novel test-time scaling strategy that leverages the model's native visual predictions as guidance to improve SVG decoding quality.
  arXiv  Detail & Related papers  (2025-12-11T18:23:03Z)
• Decomate: Leveraging Generative Models for Co-Creative SVG Animation [0.4077787659104315]
  Decomate enables intuitive animation through natural language.<n>System restructures raw SVGs into semantically meaningful, animation-ready components.<n>By supporting iterative refinement through natural language interaction, Decomate integrates generative AI into creative.
  arXiv  Detail & Related papers  (2025-11-09T09:28:51Z)
• InternSVG: Towards Unified SVG Tasks with Multimodal Large Language Models [65.49118879021016]
  We present the InternSVG family, an integrated data-benchmark-model suite.<n>At its core is SAgoge, the largest and most comprehensive multimodal dataset for SVG tasks.<n>We propose InternSVG, a unified MLLM for SVG understanding, editing, and generation with SVG-specific special tokens.
  arXiv  Detail & Related papers  (2025-10-13T12:38:04Z)
• SVGThinker: Instruction-Aligned and Reasoning-Driven Text-to-SVG Generation [47.390332111383294]
  We present SVGThinker, a reasoning-driven framework that aligns the production of SVG code with the visualization process.<n>Our pipeline first renders each primitive in sequence and uses a multimodal model to annotate the image and code.<n> Experiments against state-of-the-art baselines show that SVGThinker produces more stable, editable, and higher-quality SVGs.
  arXiv  Detail & Related papers  (2025-09-29T05:25:00Z)
• SVGen: Interpretable Vector Graphics Generation with Large Language Models [61.62816031675714]
  We introduce SVG-1M, a large-scale dataset of high-quality SVGs paired with natural language descriptions.<n>We create well-aligned Text to SVG training pairs, including a subset with Chain of Thought annotations for enhanced semantic guidance.<n>Based on this dataset, we propose SVGen, an end-to-end model that generates SVG code from natural language inputs.
  arXiv  Detail & Related papers  (2025-08-06T15:00:24Z)
• Rendering-Aware Reinforcement Learning for Vector Graphics Generation [15.547843461605746]
  We introduce RLRF(Reinforcement Learning from Rendering Feedback), an RL method that enhances SVG generation in vision-language models (VLMs)<n>Given an input image, the model generates SVG roll-outs that are rendered and compared to the original image to compute a reward.<n>This visual fidelity feedback guides the model toward producing more accurate, efficient, and semantically coherent SVGs.
  arXiv  Detail & Related papers  (2025-05-27T06:56:00Z)
• NeuralSVG: An Implicit Representation for Text-to-Vector Generation [54.4153300455889]
  We propose NeuralSVG, an implicit neural representation for generating vector graphics from text prompts.<n>To encourage a layered structure in the generated SVG, we introduce a dropout-based regularization technique.<n>We demonstrate that NeuralSVG outperforms existing methods in generating structured and flexible SVG.
  arXiv  Detail & Related papers  (2025-01-07T18:50:06Z)
• DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation [217.86315551526235]
  We propose a novel hierarchical generative network, called DeepSVG, for complex SVG icons generation and manipulation. Our architecture effectively disentangles high-level shapes from the low-level commands that encode the shape itself. We demonstrate that our network learns to accurately reconstruct diverse vector graphics, and can serve as a powerful animation tool.
  arXiv  Detail & Related papers  (2020-07-22T09:36:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.