This paper introduces a novel data-driven motion in-betweening system to reach target poses of characters by making use of phases variables learned by a Periodic Autoencoder. Our approach utilizes a mixture-of-experts neural network model, in which the phases cluster movements in both space and time with different expert weights. Each generated set of weights then produces a sequence of poses in an autoregressive manner between the current and target state of the character. In addition, to satisfy poses which are manually modified by the animators or where certain end effectors serve as constraints to be reached by the animation, a learned bi-directional control scheme is implemented to satisfy such constraints. The results demonstrate that using phases for motion in-betweening tasks sharpen the interpolated movements, and furthermore stabilizes the learning process. Moreover, using phases for motion in-betweening tasks can also synthesize more challenging movements beyond locomotion behaviors. Additionally, style control is enabled between given target keyframes. Our proposed framework can compete with popular state-of-the-art methods for motion in-betweening in terms of motion quality and generalization, especially in the existence of long transition durations. Our framework contributes to faster prototyping workflows for creating animated character sequences, which is of enormous interest for the game and film industry.

翻译：本文提出了一种新颖的数据驱动运动插帧系统，利用周期自编码器学习的相位变量实现角色目标姿态的匹配。我们的方法采用混合专家神经网络模型，通过不同专家权重在空间和时间上对运动进行相位聚类。生成的每组权重以自回归方式在角色当前状态与目标状态之间生成姿态序列。此外，为满足动画师手动修改的姿态或需通过动画达到特定末端执行器约束的要求，我们实现了一种双向控制方案以施加此类约束。实验结果表明，将相位用于运动插帧任务可锐化插值运动，并稳定学习过程。同时，使用相位进行运动插帧还能合成超越步态行为的更具挑战性的运动，且在给定目标关键帧间实现风格控制。与现有主流运动插帧方法相比，本框架在运动质量与泛化能力方面具有竞争力，尤其适用于长过渡时段场景。该框架可显著加速动画角色序列的快速原型设计流程，对游戏与影视行业具有重要价值。