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arXiv · 2503.08417

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

Kwan Yun·Seokhyeon Hong·Chaelin Kim·Junyong Noh
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Abstract

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.

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BibTeXRIS

Kwan Yun, Seokhyeon Hong, Chaelin Kim, Junyong Noh. 2025-03-11. AnyMoLe: Any Character Motion In-betweening Leveraging Video Diffusion Models. https://arxiv.org/abs/2503.08417

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