Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems
<|reference_start|>Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems: 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.<|reference_end|>
@article{garcia2024learning,
    title={Learning Physics From Video: Unsupervised Physical Parameter Estimation
  for Continuous Dynamical Systems},
    author={Alejandro Casta~neda Garcia, Jan van Gemert, Daan Brinks and Nergis
  T"omen},
    journal={arXiv preprint arXiv:2410.01376},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01376},
    primaryClass={cs.CV physics.comp-ph}
}