An Elastic Shape Variational Autoencoder for Skeleton Pose Trajectories

The paper introduces the Elastic Shape Variational Autoencoder (ES‑VAE), a geometry‑aware generative model for skeletal pose trajectories that uses the transported square‑root velocity field representation on Kendall's shape manifold to remove rigid transformations and temporal rate variability. ES‑VAE maps sequences to a low‑dimensional latent space via the Riemannian logarithm map and reconstructs them using the exponential map. Experiments on gait analysis for clinical mobility scoring and action recognition on the NTU RGB+D dataset show that ES‑VAE outperforms standard VAEs and several sequence‑modeling baselines.

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