  Conditional Normalizing Flows (CNFs) are flexible generative models capable
of representing complicated distributions with high dimensionality and large
interdimensional correlations, making them appealing for structured output
learning. Their effectiveness in modelling multivariates spatio-temporal
structured data has yet to be completely investigated. We propose MotionFlow as
a novel normalizing flows approach that autoregressively conditions the output
distributions on the spatio-temporal input features. It combines deterministic
and stochastic representations with CNFs to create a probabilistic neural
generative approach that can model the variability seen in high dimensional
structured spatio-temporal data. We specifically propose to use conditional
priors to factorize the latent space for the time dependent modeling. We also
exploit the use of masked convolutions as autoregressive conditionals in CNFs.
As a result, our method is able to define arbitrarily expressive output
probability distributions under temporal dynamics in multivariate prediction
tasks. We apply our method to different tasks, including trajectory prediction,
motion prediction, time series forecasting, and binary segmentation, and
demonstrate that our model is able to leverage normalizing flows to learn
complicated time dependent conditional distributions.
