  Motion in-betweening (MIB) is a process of generating intermediate skeletal
movement between the given start and target poses while preserving the
naturalness of the motion, such as periodic footstep motion while walking.
Although state-of-the-art MIB methods are capable of producing plausible
motions given sparse key-poses, they often lack the controllability to generate
motions satisfying the semantic contexts required in practical applications. We
focus on the method that can handle pose or semantic conditioned MIB tasks
using a unified model. We also present a motion augmentation method to improve
the quality of pose-conditioned motion generation via defining a distribution
over smooth trajectories. Our proposed method outperforms the existing
state-of-the-art MIB method in pose prediction errors while providing
additional controllability.
