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MotionFlow: Attention-Driven Motion Transfer in Video Diffusion Models

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arxiv 2412.05275 v1 pith:WTRUD3MW submitted 2024-12-06 cs.CV cs.AI

Tuna Han Salih Meral , Hidir Yesiltepe , Connor Dunlop , Pinar Yanardag This is my paper · ORCID
classification cs.CVcs.AI
keywords modelsmotionmotionflowvideodiffusioncapabilitiesscenetransfer
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LoRAShop: Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transformers

    cs.CV 2025-05 conditional novelty 7.0 of 10

    LoRAShop localizes each LoRA's effect to attention-derived spatial masks inside a Flux transformer, enabling training-free multi-concept image generation and editing.

  2. ElasticTTT: Prior-Preserving Test-Time Tuning for Video Editing

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Test-time tuning of video diffusion models collapses generation toward the source video; ElasticTTT counters this with noisy targets, contrastive source-prompt guidance, and asynchronous region-wise noise scheduling, ...

  3. MotionShot: Adaptive Motion Transfer across Arbitrary Objects for Text-to-Video Generation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    MotionShot transfers motion from a reference video to an unseen target object in text-to-video generation by combining semantic and morphological alignment in a training-free pipeline.

  4. STR-Match: Matching SpatioTemporal Relevance Score for Training-Free Video Editing

    cs.CV 2025-06 conditional novelty 6.0 of 10

    STR-Match edits videos without retraining by matching source and target 'spatiotemporal relevance scores' derived from attention maps during latent optimization.

  5. LMP: Leveraging Motion Prior in Zero-Shot Video Generation with Diffusion Transformer

    cs.CV 2025-05 conditional novelty 6.0 of 10

    LMP transfers motion from a reference video to newly generated videos in text-to-video and image-to-video settings without training, using attention maps in a frozen diffusion transformer.

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