Flow-based Spatio-Temporal Structured Prediction of Dynamics. (arXiv:2104.04391v2 [cs.CV] UPDATED)

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 …

arxiv cv dynamics flow prediction temporal