  Human behavior is a continuous stochastic spatio-temporal process which is
governed by semantic actions and affordances as well as latent factors.
Therefore, video-based human activity modeling is concerned with a number of
tasks such as inferring current and future semantic labels, predicting future
continuous observations as well as imagining possible future label and feature
sequences. In this paper we present a semi-supervised probabilistic deep latent
variable model that can represent both discrete labels and continuous
observations as well as latent dynamics over time. This allows the model to
solve several tasks at once without explicit fine-tuning. We focus here on the
tasks of action classification, detection, prediction and anticipation as well
as motion prediction and synthesis based on 3D human activity data recorded
with Kinect. We further extend the model to capture hierarchical label
structure and to model the dependencies between multiple entities, such as a
human and objects. Our experiments demonstrate that our principled approach to
human activity modeling can be used to detect current and anticipate future
semantic labels and to predict and synthesize future label and feature
sequences. When comparing our model to state-of-the-art approaches, which are
specifically designed for e.g. action classification, we find that our
probabilistic formulation outperforms or is comparable to these task specific
models.
