Adaptive Interpolation-Synthesis for Motion In-Betweening on Keyframe-Based Animation

Fig. 1. Our novel Adaptive Interpolation-Synthesis (AIS) layer, combined with a Bi-LSTM encoder, generates dense 3D animation (bottom) from sparse block poses (top). It produces accurate intermediate poses while preserving motion style, yielding high-quality results that require only minor retakes and accelerate the in-betweening process by up to 3.5×.

Motion in-betweening is one of the most artistically demanding and timeconsuming 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 3.5× speedup.