  We introduce a representation learning framework for spatial trajectories. We
represent partial observations of trajectories as probability distributions in
a learned latent space, which characterize the uncertainty about unobserved
parts of the trajectory. Our framework allows us to obtain samples from a
trajectory for any continuous point in time, both interpolating and
extrapolating. Our flexible approach supports directly modifying specific
attributes of a trajectory, such as its pace, as well as combining different
partial observations into single representations. Experiments show our method's
advantage over baselines in prediction tasks.
