Motion in-betweening is one of the most artistically demanding and time consuming stages of 3D animation, where the expressivity and rhythm of motion are defined. The level of creative control it requires makes it a major production bottleneck, underscoring the need for intelligent tools that assist animators in this process. Although recent deep learning approaches have achieved strong results in motion synthesis and in-betweening, they assume data characteristics, motion styles, and problem formulations that diverge from professional animation workflows. To bridge this gap, we propose a method explicitly aligned with the constraints of motion in-betweening for keyframe-based animation in production environments. At its core, the Adaptive Interpolation-Synthesis (AIS) layer mirrors the animator's creative process by dynamically balancing learned interpolation and direct pose synthesis. In addition, a domain-based input keypose schedule reflects the distribution of production data, improving stylistic consistency and alignment between training and real-world usage. Our method achieves state-of-the-art performance on production data; when integrated into Autodesk Maya, it enables animators to complete in-betweening tasks with a 3.5x speedup.

翻译：运动补间是三维动画中最具艺术表现要求且耗时的环节之一，该阶段决定了运动的表达性与节奏感。其所需要的创造性控制程度使其成为主要生产瓶颈，凸显了开发辅助动画师完成此流程的智能工具的必要性。尽管近年来深度学习方法在运动合成与补间领域取得了显著成果，但这些方法所假设的数据特征、运动风格及问题定义方式与专业动画工作流程存在差异。为弥合这一鸿沟，我们提出一种与生产环境下关键帧动画运动补间约束严格对齐的方法。其核心组件——自适应插值-合成（AIS）层——通过动态平衡学习型插值与直接姿态合成，模拟动画师的创作过程。此外，基于领域的输入关键帧调度机制反映了生产数据分布规律，提升了训练阶段与实际应用之间风格一致性与对齐程度。该方法在生产数据上达到了当前最优性能；当集成至Autodesk Maya后，动画师完成补间任务的速度提升了3.5倍。