Styled online in-between motion generation has important application scenarios in computer animation and games. Its core challenge lies in the need to satisfy four critical requirements simultaneously: generation speed, motion quality, style diversity, and synthesis controllability. While the first two challenges demand a delicate balance between simple fast models and learning capacity for generation quality, the latter two are rarely investigated together in existing methods, which largely focus on either control without style or uncontrolled stylized motions. To this end, we propose a Real-time Stylized Motion Transition method (RSMT) to achieve all aforementioned goals. Our method consists of two critical, independent components: a general motion manifold model and a style motion sampler. The former acts as a high-quality motion source and the latter synthesizes styled motions on the fly under control signals. Since both components can be trained separately on different datasets, our method provides great flexibility, requires less data, and generalizes well when no/few samples are available for unseen styles. Through exhaustive evaluation, our method proves to be fast, high-quality, versatile, and controllable. The code and data are available at {https://github.com/yuyujunjun/RSMT-Realtime-Stylized-Motion-Transition.}

翻译：风格化在线插值运动生成技术在计算机动画和游戏领域具有重要的应用场景。其核心挑战在于需同时满足四项关键需求：生成速度、运动质量、风格多样性及合成可控性。前两项挑战要求在简单快速模型与生成质量学习能力之间取得精妙平衡，而后两项需求在现有方法中鲜有共同研究——现有方法多侧重于无风格控制或无控制风格化运动。为此，我们提出实时风格化运动过渡方法（RSMT）以同时实现上述目标。该方法包含两个独立的关键组件：通用运动流形模型与风格运动采样器。前者作为高质量运动源，后者则在控制信号驱动下实时合成风格化运动。由于两个组件可分别在不同数据集上训练，本方法具备高度灵活性、数据需求更低，且能在缺乏/少量未见过风格样本时展现良好泛化能力。通过全面评估，证明本方法兼具快速性、高质量、多功能性与可控性。代码与数据可从{https://github.com/yuyujunjun/RSMT-Realtime-Stylized-Motion-Transition.}获取。