  We introduce a spatio-temporal convolutional neural network model for
trajectory forecasting from visual sources. Applied in an auto-regressive way
it provides an explicit probability distribution over continuations of a given
initial trajectory segment. We discuss it in relation to (more complicated)
existing work and report on experiments on two standard datasets for trajectory
forecasting: MNISTseq and Stanford Drones, achieving results on-par with or
better than previous methods.
