  Prediction of trajectories such as that of pedestrians is crucial to the
performance of autonomous agents. While previous works have leveraged
conditional generative models like GANs and VAEs for learning the likely future
trajectories, accurately modeling the dependency structure of these multimodal
distributions, particularly over long time horizons remains challenging.
Normalizing flow based generative models can model complex distributions
admitting exact inference. These include variants with split coupling
invertible transformations that are easier to parallelize compared to their
autoregressive counterparts. To this end, we introduce a novel Haar wavelet
based block autoregressive model leveraging split couplings, conditioned on
coarse trajectories obtained from Haar wavelet based transformations at
different levels of granularity. This yields an exact inference method that
models trajectories at different spatio-temporal resolutions in a hierarchical
manner. We illustrate the advantages of our approach for generating diverse and
accurate trajectories on two real-world datasets - Stanford Drone and
Intersection Drone.
