  Estimating future events is a difficult task. Unlike humans, machine learning
approaches are not regularized by a natural understanding of physics. In the
wild, a plausible succession of events is governed by the rules of causality,
which cannot easily be derived from a finite training set. In this paper we
propose a novel theoretical framework to perform causal future prediction by
embedding spatiotemporal information on a Minkowski space-time. We utilize the
concept of a light cone from special relativity to restrict and traverse the
latent space of an arbitrary model. We demonstrate successful applications in
causal image synthesis and future video frame prediction on a dataset of
images. Our framework is architecture- and task-independent and comes with
strong theoretical guarantees of causal capabilities.
