Variational Autoencoder with Differentiable Physics Engine for Human Gait Analysis and SynthesisDownload PDF

Published: 08 Dec 2021, Last Modified: 05 May 2023DGMs and Applications @ NeurIPS 2021 PosterReaders: Everyone
Keywords: variational autoencoder, differentiable physics engine, human gait
Abstract: We address the task of learning generative models of human gait. As gait motion always follows the physical laws, a generative model should also produce outputs that comply with the physical laws, particularly rigid body dynamics with contact and friction. We propose a deep generative model combined with a differentiable physics engine, which outputs physically plausible signals by construction. The proposed model is also equipped with a policy network conditioned on each sample. We show an example of the application of such a model to style transfer of gait.
TL;DR: We use a generative model integrated with a differentiable physics engine for modeling human gait.
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