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.
View on arXiv@article{szewczyk2025_2409.15344,
title={ Video-Driven Graph Network-Based Simulators },
author={ Franciszek Szewczyk and Gilles Louppe and Matthia Sabatelli },
journal={arXiv preprint arXiv:2409.15344},
year={ 2025 }
}