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Variational Autoencoder with Differentiable Physics Engine for Human Gait Analysis and Synthesis
Naoya Takeishi · Alexandros Kalousis
Event URL: https://openreview.net/forum?id=9ISlKio3Bt »

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.

Author Information

Naoya Takeishi (HES-SO / RIKEN)
Alexandros Kalousis

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