Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.09104 (cs)
[Submitted on 10 Mar 2026]
Title:Training-free Motion Factorization for Compositional Video Generation
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Abstract:Compositional video generation aims to synthesize multiple instances with diverse appearance and motion, which is widely applicable in real-world scenarios. However, current approaches mainly focus on binding semantics, neglecting to understand diverse motion categories specified in prompts. In this paper, we propose a motion factorization framework that decomposes complex motion into three primary categories: motionlessness, rigid motion, and non-rigid motion. Specifically, our framework follows a planning before generation paradigm. (1) During planning, we reason about motion laws on the motion graph to obtain frame-wise changes in the shape and position of each instance. This alleviates semantic ambiguities in the user prompt by organizing it into a structured representation of instances and their interactions. (2) During generation, we modulate the synthesis of distinct motion categories in a disentangled manner. Conditioned on the motion cues, guidance branches stabilize appearance in motionless regions, preserve rigid-body geometry, and regularize local non-rigid deformations. Crucially, our two modules are model-agnostic, which can be seamlessly incorporated into various diffusion model architectures. Extensive experiments demonstrate that our framework achieves impressive performance in motion synthesis on real-world benchmarks. Our code will be released soon.
| Comments: | |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2603.09104 [cs.CV] |
| (or arXiv:2603.09104v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09104
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View a PDF of the paper titled Training-free Motion Factorization for Compositional Video Generation, by Zixuan Wang and 6 other authors
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