Vector Prism：通过语义结构分层实现矢量图形动画
Vector Prism: Animating Vector Graphics by Stratifying Semantic Structure
把 SVG 中碎片化的低层图元通过多次弱预测的统计聚合恢复成语义部件组，为 VLM 提供结构化输入，这一「先恢复语义结构再交给 VLM 生成」的范式可迁移到其他需要 VLM 操作结构化图形/代码的视觉任务。
原文依据 This is achieved through a statistical aggregation of multiple weak part predictions, allowing the system to stably infer semantics from noisy predictions.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论可缩放矢量图形（SVG）是现代网页设计的核心，随着网页环境日益动态化，对其动画化的需求持续增长。然而，尽管代码生成与运动规划近期取得进展，视觉语言模型（VLM）在自动化矢量图形动画方面仍面临挑战。VLM 经常错误处理 SVG，因为视觉上连贯的部件往往被拆分为低层形状，几乎无法提供哪些元素应当一起运动的指引。本文提出一个框架，恢复可靠 SVG 动画所需的语义结构，并揭示当前 VLM 系统所忽视的缺失层。该框架通过对多个弱部件预测进行统计聚合实现，使系统能够从含噪预测中稳定地推断语义。通过将 SVG 重组为语义组，本方法使 VLM 能够生成连贯性远更高的动画。实验表明，相较现有方法取得显著提升，说明语义恢复是解锁稳健 SVG 动画的关键步骤，并支持 VLM 与矢量图形之间更具可解释性的交互。
英文原文摘要 1185 字符
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.