跨类别动作迁移不依赖外形对应，通过抽象动作表征实现
Motion Beyond Morphology: Bootstrapping Cross-Category Motion Transfer from Abstract Motion Representations
- 用多粒度抽象动作视图构建可迁移的跨类别动作对
- 在不同形态物体间保持动作保真度，优于现有方法
- 适合跨类别动画生成，尤其适用于形变差异大的对象
视频动作迁移旨在使用参考视频中的动态为目标物体赋予动作。现有方法大多依赖固定结构对应关系，当参考与目标物体在形态、关节或变形机制上差异较大时，该假设失效。本文提出「超越形态的动作迁移」新视角，通过保留跨不同形态仍具意义的动态来实现动作迁移。我们设计两阶段框架：第一阶段学习互补的多粒度抽象动作表示，并据此构建跨类别视频对，以保留可迁移的动态；第二阶段将此监督内化为直接的参考视频条件生成，推理时无需显式提取动作。此外，我们构建了 OpenVMT-Dataset 与 OpenVMT-Bench，用于训练和评估图像及文本条件下的跨类别动作迁移，涵盖同类别、近类别和远类别差距。大量实验表明，本方法在动作保真度和目标保持方面达到当前最优水平。
原文摘要 · Abstract (English)
Video motion transfer aims to animate a target object using dynamics from a reference video. Existing formulations largely rely on fixed structural correspondence, which becomes ill-defined when reference and target objects differ substantially in morphology, articulation, or deformation mechanisms. We introduce Motion Beyond Morphology, a perspective that seeks to transfer motion beyond fixed structural correspondence, by preserving dynamics that remain meaningful across different target morphologies. To realize this, we propose a two-stage framework. Stage~I learns complementary multi-granularity abstract motion views and uses them to bootstrap cross-category video pairs that preserve transferable dynamics across diverse morphologies. Stage~II internalizes this supervision into direct reference-video-conditioned generation, removing the need for explicit motion extraction at inference. We further introduce OpenVMT-Dataset and OpenVMT-Bench for training and evaluating image- and text-conditioned motion transfer across Same, Near, and Far category gaps. Extensive experiments demonstrate state-of-the-art motion fidelity and target preservation. Project page: https://miniz233.github.io/MotionBeyondMorphology/
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