  The generation of plausible and controllable 3D human motion animations is a
long-standing problem that often requires a manual intervention of skilled
artists. Existing machine learning approaches try to semi-automate this process
by allowing the user to input partial information about the future movement.
However, they are limited in two significant ways: they either base their pose
prediction on past prior frames with no additional control over the future
poses or allow the user to input only a single trajectory that precludes
fine-grained control over the output. To mitigate these two issues, we
reformulate the problem of future pose prediction into pose completion in space
and time where trajectories are represented as poses with missing joints. We
show that such a framework can generalize to other neural networks designed for
future pose prediction. Once trained in this framework, a model is capable of
predicting sequences from any number of trajectories. To leverage this notion,
we propose a novel transformer-like architecture, TrajeVAE, that provides a
versatile framework for 3D human animation. We demonstrate that TrajeVAE
outperforms trajectory-based reference approaches and methods that base their
predictions on past poses in terms of accuracy. We also show that it can
predict reasonable future poses even if provided only with an initial pose.
