Despite recent advancements in learning-based motion in-betweening, a key limitation has been overlooked: the requirement for character-specific datasets. In this work, we introduce AnyMoLe, a novel method that addresses this limitation by leveraging video diffusion models to generate motion in-between frames for arbitrary characters without external data. Our approach employs a two-stage frame generation process to enhance contextual understanding. Furthermore, to bridge the domain gap between real-world and rendered character animations, we introduce ICAdapt, a fine-tuning technique for video diffusion models. Additionally, we propose a ``motion-video mimicking'' optimization technique, enabling seamless motion generation for characters with arbitrary joint structures using 2D and 3D-aware features. AnyMoLe significantly reduces data dependency while generating smooth and realistic transitions, making it applicable to a wide range of motion in-betweening tasks.

本研究解决了特定角色数据集的需求问题，提出了一种新方法AnyMoLe，利用视频扩散模型为任意角色生成运动插值帧。通过引入ICAdapt微调技术和"运动视频模仿"优化方法，该方法显著降低了数据依赖性，能够生成平滑逼真的过渡效果，适用于多种运动插值任务。

AnyMoLe：利用视频扩散模型进行任意角色运动插值