  In this paper, we propose a Bayesian switching dynamical model for
segmentation of 3D pose data over time that uncovers interpretable patterns in
the data and is generative. Our model decomposes highly correlated skeleton
data into a set of few spatial basis of switching temporal processes in a
low-dimensional latent framework. We parameterize these temporal processes with
regard to a switching deep vector autoregressive prior in order to accommodate
both multimodal and higher-order nonlinear inter-dependencies. This results in
a dynamical deep generative latent model that parses the meaningful intrinsic
states in the dynamics of 3D pose data using approximate variational inference,
and enables a realistic low-level dynamical generation and segmentation of
complex skeleton movements. Our experiments on four biological motion data
containing bat flight, salsa dance, walking, and golf datasets substantiate
superior performance of our model in comparison with the state-of-the-art
methods.
