MotionFlow: Attention-Driven Motion Transfer in Video Diffusion Models

• URL: http://arxiv.org/abs/2412.05275v1
• Date: Fri, 06 Dec 2024 18:59:12 GMT
• Title: MotionFlow: Attention-Driven Motion Transfer in Video Diffusion Models
• Authors: Tuna Han Salih Meral, Hidir Yesiltepe, Connor Dunlop, Pinar Yanardag,
• Abstract summary: We introduce MotionFlow, a novel framework designed for motion transfer in video diffusion models.<n>Our method utilizes cross-attention maps to accurately capture and manipulate spatial and temporal dynamics.<n>Our experiments demonstrate that MotionFlow significantly outperforms existing models in both fidelity and versatility even during drastic scene alterations.
• Score: 3.2311303453753033
• License: http://creativecommons.org/licenses/by/4.0/
• 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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