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.

翻译：在设计、电影摄影和游戏领域中，逼真的可视化效果依赖于精确的物理模拟，这通常需要大量的计算资源和详细的物理参数输入。本文提出一种方法，能够从短视频中推断系统的物理属性，从而在接近训练条件的情况下，无需显式的参数输入。学习到的表征随后被用于基于图网络的模拟器中，以仿真物理系统的运动轨迹。我们证明，从视频中提取的编码能有效捕捉系统的物理特性，并展示其中部分编码与系统运动之间存在线性依赖关系。