通过重构语义结构，让AI更准确地动画化矢量图
Vector Prism: Animating Vector Graphics by Stratifying Semantic Structure
- 用多弱预测聚合恢复矢量图的语义结构
- 使AI生成的动画一致性显著提升
- 适合需要精准矢量动画的网页开发者
可缩放矢量图形（SVG）是现代网页设计的核心，随着网络环境日益动态，对动画的需求持续增长。然而，尽管代码生成和运动规划取得进展，视觉语言模型（VLMs）在自动化矢量图形动画方面仍面临挑战，主要因为视觉连贯的部分常被拆分为低级形状，缺乏元素应共同移动的指导信息。本文提出一种框架，通过统计聚合多个弱部分预测，稳定地从噪声预测中恢复语义结构，填补当前VLM系统忽视的关键层。该方法将SVG重新组织为语义分组，使VLM能够生成更一致的动画。实验表明，本方法显著优于现有方案，证明语义恢复是实现鲁棒矢量动画的关键步骤，并支持VLM与矢量图形之间更可解释的交互。
原文摘要 · Abstract (English)
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.
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