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).

翻译：在游戏中普遍需要在运动间实时产生运动,而现有的动画管道则非常适宜。其核心挑战在于需要同时满足三个关键条件:质量、可控性和速度,这使得任何需要离线计算(或后处理)或不能(通常不可预测的)用户控制的方法都不受欢迎。为此,我们提出一个新的实时过渡方法,以应对上述挑战。我们的方法由两个关键组成部分组成:运动多重和有条件的过渡。前者了解重要的低级运动特征及其动态;后者综合过渡以目标框架和预期的过渡期限为条件。我们首先学习一个运动组合,通过多模式绘图机制明确模拟人类运动的内在过渡随机性。随后,我们设计了一个过渡模式,基本上是一种抽样战略,从所学的柱体取样,以目标框架和预定的过渡期限为基础。我们在不允许后处理或离线计算的任务中验证我们采用的不同数据集的方法。通过详尽的评估和比较,我们显示我们的方法能够通过多种衡量方法,以高质量的模型衡量。