从视频中自动推断物理属性，驱动图网络模拟系统运动
Video-Driven Graph Network-Based Simulators
- 用视频编码提取物理属性，替代人工输入参数
- 编码与系统运动呈线性关系，可准确预测轨迹
- 适合需要快速建模的影视游戏场景
设计、影视和游戏中逼真的视觉效果依赖于精确的物理模拟，通常需大量计算资源和详细的物理参数输入。本文提出一种方法，仅需一段短视频即可推断系统的物理属性，无需显式参数输入，前提是系统状态接近训练条件。学习到的表示被用于基于图网络的模拟器，以重现物理系统的运动轨迹。实验表明，视频生成的编码能有效捕捉系统物理特性，并揭示部分编码与系统运动之间存在线性关系。
原文摘要 · Abstract (English)
Lifelike visualizations in design, cinematography, and gaming rely on precise physics simulations, typically requiring extensive computational resources and detailed physical input. This paper presents a method that can infer a system's physical properties from a short video, eliminating the need for explicit parameter input, provided it is close to the training condition. The learned representation is then used within a Graph Network-based Simulator to emulate the trajectories of physical systems. We demonstrate that the video-derived encodings effectively capture the physical properties of the system and showcase a linear dependence between some of the encodings and the system's motion.
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