AnyMoLe：基於視頻擴散模型的任意角色動作插值技術

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

March 11, 2025

作者: Kwan Yun, Seokhyeon Hong, Chaelin Kim, Junyong Noh

cs.AI

摘要

儘管基於學習的運動插值技術近期取得了進展，但一個關鍵限制卻被忽視了：對角色特定數據集的需求。在本研究中，我們提出了AnyMoLe，這是一種新穎的方法，通過利用視頻擴散模型來生成任意角色的運動插值幀，無需外部數據，從而解決了這一限制。我們的方法採用兩階段幀生成過程來增強上下文理解。此外，為了彌合現實世界與渲染角色動畫之間的領域差距，我們引入了ICAdapt，這是一種針對視頻擴散模型的微調技術。同時，我們提出了一種“運動-視頻模仿”優化技術，使得利用2D和3D感知特徵為具有任意關節結構的角色實現無縫運動生成成為可能。AnyMoLe在生成平滑且逼真的過渡效果時，顯著降低了數據依賴性，使其適用於廣泛的運動插值任務。

English

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.