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.

本研究解决了从视频中提取物理动力系统参数的难题，克服了现有无监督技术的局限性，如长训练时间和不稳定性。通过引入基于KL散度的损失函数，提出了一种新方法，使得对已知连续控制方程的物理参数进行估计更加稳健，并减小了模型规模和计算量。

从视频中学习物理：连续动力系统的无监督物理参数估计