  Trajectory generation is an important task in movement studies; it
circumvents the privacy, ethical, and technical challenges of collecting real
trajectories from the target population. In particular, real trajectories in
the wildlife domain are scarce as a result of ethical and environmental
constraints of the collection process. In this paper, we consider the problem
of generating long-horizon trajectories, akin to wildlife migration, based on a
small set of real samples. We propose a hierarchical approach to learn the
global movement characteristics of the real dataset and recursively refine
localized regions. Our solution, WildGraph, discretizes the geographic path
into a prototype network of H3 (https://www.uber.com/blog/h3/) regions and
leverages a recurrent variational auto-encoder to probabilistically generate
paths over the regions, based on occupancy. WildGraph successfully generates
realistic months-long trajectories using a sample size as small as 60.
Experiments performed on two wildlife migration datasets demonstrate that our
proposed method improves the generalization of the generated trajectories in
comparison to existing work while achieving superior or comparable performance
in several benchmark metrics. Our code is published on the following
repository: \url{https://github.com/aliwister/wildgraph}.
