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arXiv · 2412.07750

Motion by Queries: Identity-Motion Trade-offs in Text-to-Video Generation

Yuval Atzmon·Rinon Gal·Yoad Tewel·Yoni Kasten·Gal Chechik
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Abstract

Text-to-video diffusion models have shown remarkable progress in generating coherent video clips from textual descriptions. However, the interplay between motion, structure, and identity representations in these models remains under-explored. Here, we investigate how self-attention query (Q) features simultaneously govern motion, structure, and identity and examine the challenges arising when these representations interact. Our analysis reveals that Q affects not only layout, but that during denoising Q also has a strong effect on subject identity, making it hard to transfer motion without the side-effect of transferring identity. Understanding this dual role enabled us to control query feature injection (Q injection) and demonstrate two applications: (1) a zero-shot motion transfer method - implemented with VideoCrafter2 and WAN 2.1 - that is 10 times more efficient than existing approaches, and (2) a training-free technique for consistent multi-shot video generation, where characters maintain identity across multiple video shots while Q injection enhances motion fidelity.

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Yuval Atzmon, Rinon Gal, Yoad Tewel, Yoni Kasten, Gal Chechik. 2024-12-10. Motion by Queries: Identity-Motion Trade-offs in Text-to-Video Generation. https://arxiv.org/abs/2412.07750

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