[2409.15344] Video-Driven Graph Network-Based Simulators
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2409.15344 (cs)
[Submitted on 10 Sep 2024 (v1), last revised 2 Dec 2024 (this version, v2)]

Title:Video-Driven Graph Network-Based Simulators

Authors:Franciszek Szewczyk, Gilles Louppe, Matthia Sabatelli
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Abstract: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.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2409.15344 [cs.CV]
  (or arXiv:2409.15344v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2409.15344

Submission history

From: Matthia Sabatelli [view email]
[v1] Tue, 10 Sep 2024 07:04:48 UTC (6,803 KB)
[v2] Mon, 2 Dec 2024 09:45:07 UTC (6,810 KB)
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