ES-VAE is a variational autoencoder for skeleton trajectories that uses the transported square-root velocity field on Kendall's shape manifold to remove rigid motions and timing variations, outperforming standard VAEs on gait analysis and action recognition.
Learning weighted submanifolds with variational autoencoders and
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An Elastic Shape Variational Autoencoder for Skeleton Pose Trajectories
ES-VAE is a variational autoencoder for skeleton trajectories that uses the transported square-root velocity field on Kendall's shape manifold to remove rigid motions and timing variations, outperforming standard VAEs on gait analysis and action recognition.