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arXiv · 2412.05275

MotionFlow: Attention-Driven Motion Transfer in Video Diffusion Models

Tuna Han Salih Meral·Hidir Yesiltepe·Connor Dunlop·Pinar Yanardag
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Abstract

Text-to-video models have demonstrated impressive capabilities in producing diverse and captivating video content, showcasing a notable advancement in generative AI. However, these models generally lack fine-grained control over motion patterns, limiting their practical applicability. We introduce MotionFlow, a novel framework designed for motion transfer in video diffusion models. Our method utilizes cross-attention maps to accurately capture and manipulate spatial and temporal dynamics, enabling seamless motion transfers across various contexts. Our approach does not require training and works on test-time by leveraging the inherent capabilities of pre-trained video diffusion models. In contrast to traditional approaches, which struggle with comprehensive scene changes while maintaining consistent motion, MotionFlow successfully handles such complex transformations through its attention-based mechanism. Our qualitative and quantitative experiments demonstrate that MotionFlow significantly outperforms existing models in both fidelity and versatility even during drastic scene alterations.

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Tuna Han Salih Meral, Hidir Yesiltepe, Connor Dunlop, Pinar Yanardag. 2024-12-06. MotionFlow: Attention-Driven Motion Transfer in Video Diffusion Models. https://arxiv.org/abs/2412.05275

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