  Real-time in-between motion generation is universally required in games and
highly desirable in existing animation pipelines. Its core challenge lies in
the need to satisfy three critical conditions simultaneously: quality,
controllability and speed, which renders any methods that need offline
computation (or post-processing) or cannot incorporate (often unpredictable)
user control undesirable. To this end, we propose a new real-time transition
method to address the aforementioned challenges. Our approach consists of two
key components: motion manifold and conditional transitioning. The former
learns the important low-level motion features and their dynamics; while the
latter synthesizes transitions conditioned on a target frame and the desired
transition duration. We first learn a motion manifold that explicitly models
the intrinsic transition stochasticity in human motions via a multi-modal
mapping mechanism. Then, during generation, we design a transition model which
is essentially a sampling strategy to sample from the learned manifold, based
on the target frame and the aimed transition duration. We validate our method
on different datasets in tasks where no post-processing or offline computation
is allowed. Through exhaustive evaluation and comparison, we show that our
method is able to generate high-quality motions measured under multiple
metrics. Our method is also robust under various target frames (with extreme
cases).
