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arXiv · 2608.01628

Motion Beyond Morphology: Bootstrapping Cross-Category Motion Transfer from Abstract Motion Representations

Zhixue Fang·Zhimin Zhang·Bi'an Du·Zijie Meng·Yan Zhou·Wei Hu·Guoxin Zhang·Pengfei Wan·Kun Gai
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Abstract

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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Zhixue Fang, Zhimin Zhang, Bi'an Du, Zijie Meng, Yan Zhou, Wei Hu, Guoxin Zhang, Pengfei Wan, Kun Gai. 2026-08-04. Motion Beyond Morphology: Bootstrapping Cross-Category Motion Transfer from Abstract Motion Representations. https://arxiv.org/abs/2608.01628

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