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MotionShop: Zero-Shot Motion Transfer in Video Diffusion Models with Mixture of Score Guidance

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arxiv 2412.05355 v1 pith:DTGPQOYN submitted 2024-12-06 cs.CV cs.AI

Hidir Yesiltepe , Tuna Han Salih Meral , Connor Dunlop , Pinar Yanardag This is my paper
classification cs.CVcs.AI
keywords motiontransferdiffusionscoremodelsmixturecameracomplex
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In this work, we propose the first motion transfer approach in diffusion transformer through Mixture of Score Guidance (MSG), a theoretically-grounded framework for motion transfer in diffusion models. Our key theoretical contribution lies in reformulating conditional score to decompose motion score and content score in diffusion models. By formulating motion transfer as a mixture of potential energies, MSG naturally preserves scene composition and enables creative scene transformations while maintaining the integrity of transferred motion patterns. This novel sampling operates directly on pre-trained video diffusion models without additional training or fine-tuning. Through extensive experiments, MSG demonstrates successful handling of diverse scenarios including single object, multiple objects, and cross-object motion transfer as well as complex camera motion transfer. Additionally, we introduce MotionBench, the first motion transfer dataset consisting of 200 source videos and 1000 transferred motions, covering single/multi-object transfers, and complex camera motions.

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Cited by 4 Pith papers

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

  1. QWERTY: Training-Free Motion Control via Query-Warped Video Diffusion Transformers

    cs.CV 2026-07 unverdicted novelty 7.0 of 10

    QWERTY enables training-free motion control in pretrained image-to-video DiTs by warping the frame-invariant semantic subspace of queries in 3D full attention and using the predicted noise as self-guidance for latent ...

  2. DTG-Restore: Training-Free Diffusion Refinement for Generative Video Super-Resolution

    cs.CV 2026-05 unverdicted novelty 7.0 of 10

    Presents Decoupled Time Guidance (DTG) for training-free generative video super-resolution by temporally decoupling conditional and unconditional diffusion signals.

  3. Motion Beyond Morphology: Bootstrapping Cross-Category Motion Transfer from Abstract Motion Representations

    cs.CV 2026-08 conditional novelty 6.0 of 10

    A two-stage framework bootstraps cross-category motion transfer by training on self-generated motion-equivalent video pairs, enabling direct reference-video-conditioned animation across morphology gaps.

  4. SynMotion: Semantic-Visual Adaptation for Motion Customized Video Generation

    cs.CV 2025-06 unverdicted novelty 5.0 of 10

    SynMotion combines disentangled semantic embeddings, parameter-efficient motion adapters, and alternate subject-motion training on a new SPV dataset to improve motion customization in text-to-video and image-to-video ...

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