Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems

• URL: http://arxiv.org/abs/2410.01376v1
• Date: Wed, 2 Oct 2024 09:44:54 GMT
• Title: Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems
• Authors: Alejandro Castañeda Garcia, Jan van Gemert, Daan Brinks, Nergis Tömen,
• Abstract summary: State-of-the-art in automatic parameter estimation from video is addressed by training supervised deep networks on large datasets. We propose a method to estimate the physical parameters of any known, continuous governing equation from single videos.
• Score: 49.11170948406405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extracting physical dynamical system parameters from videos is of great interest to applications in natural science and technology. The state-of-the-art in automatic parameter estimation from video is addressed by training supervised deep networks on large datasets. Such datasets require labels, which are difficult to acquire. While some unsupervised techniques -- which depend on frame prediction -- exist, they suffer from long training times, instability under different initializations, and are limited to hand-picked motion problems. In this work, we propose a method to estimate the physical parameters of any known, continuous governing equation from single videos; our solution is suitable for different dynamical systems beyond motion and is robust to initialization compared to previous approaches. Moreover, we remove the need for frame prediction by implementing a KL-divergence-based loss function in the latent space, which avoids convergence to trivial solutions and reduces model size and compute.

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