AnyMoLe: Any Character Motion In-betweening Leveraging Video Diffusion Models

kwan yun, Seokhyeon Hong, Chaelin Kim, Junyong Noh

AnyMoLe: Any Character Motion In-betweening Leveraging Video Diffusion Models: 7 upvotes on Hugging Face Daily Papers, #22 of 40 papers on 2025-03-12. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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