向量棱柱：通过分层语义结构来动画化向量图形
Vector Prism: Animating Vector Graphics by Stratifying Semantic Structure
AI总结：
本文提出了一种通过分层语义结构来提升向量图形动画质量的框架，通过统计汇总弱预测来恢复语义，从而提高VLMs动画生成的连贯性和准确性。
摘要：
可缩放向量图形（SVG）是现代网页设计的核心，随着网页环境日益动态，对它们进行动画化的市场需求持续增长。然而，尽管在代码生成和运动规划方面取得了进展，自动化向量图形的动画化仍对视觉-语言模型（VLMs）构成挑战。VLMs通常难以处理SVG，因为视觉上连贯的部分常常被分割成低层次的形状，这些形状提供的指导很少能说明哪些元素应一起移动。在本文中，我们介绍了一个框架，该框架恢复了用于可靠SVG动画所需的语义结构，并揭示了当前VLM系统所忽视的缺失层。这通过统计汇总多个弱部分预测实现，使系统能够从嘈杂的预测中稳定推断语义。通过将SVG重新组织为语义组，我们的方法使VLMs能够产生更加连贯的动画。我们的实验表明，与现有方法相比有显著的进步，表明语义恢复是解锁稳健SVG动画的关键步骤，并支持VLMs与向量图形之间更具解释性的交互。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
