  Trajectory interpolation, the process of filling-in the gaps and removing
noise from observed agent trajectories, is an essential task for the motion
inference in multi-agent setting. A desired trajectory interpolation method
should be robust to noise, changes in environments or agent densities, while
also being yielding realistic group movement behaviors. Such realistic
behaviors are, however, challenging to model as they require avoidance of
agent-agent or agent-environment collisions and, at the same time, seek
computational efficiency. In this paper, we propose a novel framework composed
of data-driven priors (local, global or combined) and an efficient optimization
strategy for multi-agent trajectory interpolation. The data-driven priors
implicitly encode the dependencies of movements of multiple agents and the
collision-avoiding desiderata, enabling elimination of costly pairwise
collision constraints and resulting in reduced computational complexity and
often improved estimation. Various combinations of priors and optimization
algorithms are evaluated in comprehensive simulated experiments. Our
experimental results reveal important insights, including the significance of
the global flow prior and the lesser-than-expected influence of data-driven
collision priors.
