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MotionClone: Training-Free Motion Cloning for Controllable Video Generation

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arxiv 2406.05338 v6 pith:2Z4DJB5D submitted 2024-06-08 cs.CV

Pengyang Ling , Jiazi Bu , Pan Zhang , Xiaoyi Dong , Yuhang Zang , Tong Wu , Huaian Chen , Jiaqi Wang
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Yi Jin
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classification cs.CV
keywords motionmotionclonegenerationvideocloningcontrollableflexibilitytemporal
verification ladder T0 review T1 audit T2 compute T3 formal
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Motion-based controllable video generation offers the potential for creating captivating visual content. Existing methods typically necessitate model training to encode particular motion cues or incorporate fine-tuning to inject certain motion patterns, resulting in limited flexibility and generalization. In this work, we propose MotionClone, a training-free framework that enables motion cloning from reference videos to versatile motion-controlled video generation, including text-to-video and image-to-video. Based on the observation that the dominant components in temporal-attention maps drive motion synthesis, while the rest mainly capture noisy or very subtle motions, MotionClone utilizes sparse temporal attention weights as motion representations for motion guidance, facilitating diverse motion transfer across varying scenarios. Meanwhile, MotionClone allows for the direct extraction of motion representation through a single denoising step, bypassing the cumbersome inversion processes and thus promoting both efficiency and flexibility. Extensive experiments demonstrate that MotionClone exhibits proficiency in both global camera motion and local object motion, with notable superiority in terms of motion fidelity, textual alignment, and temporal consistency.

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Cited by 9 Pith papers

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

  1. Controlling Motion Transfer in Diffusion Transformers via Attention Heads

    cs.CV 2026-07 accept novelty 6.0 of 10

    Video DiTs encode motion and structure in separate attention-head subsets; selecting and guiding those heads yields training-free motion transfer with higher fidelity and structural alignment than existing methods.

  2. SymphoMotion: Joint Control of Camera Motion and Object Dynamics for Coherent Video Generation

    cs.CV 2026-04 conditional novelty 6.0 of 10

    SymphoMotion jointly controls camera trajectories and depth-aware object dynamics inside one video diffusion model, supported by the new RealCOD-25K real-world paired-motion dataset.

  3. PostCam: Camera-Controllable Novel-View Video Generation with Query-Shared Cross-Attention

    cs.CV 2025-11 conditional novelty 6.0 of 10

    PostCam generates new videos from a reference video along user-specified camera trajectories using a query-shared cross-attention that fuses pose data and rendered frames, improving control precision and detail preservation.

  4. 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.

  5. When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators

    cs.CV 2025-06 conditional novelty 6.0 of 10

    MotionEcho adaptively re-injects teacher-model guidance into few-step distilled video generators so reference motion can be copied at test time without training.

  6. IllumiCraft: Unified Geometry and Illumination Diffusion for Controllable Video Generation

    cs.CV 2025-06 reject novelty 6.0 of 10

    A diffusion video model that jointly uses HDR lighting, relit frames, and 3D point tracks to relight videos from text prompts.

  7. Consistent and Editable: A Balanced Framework for Text-Guided Video Editing

    cs.CV 2026-07 conditional novelty 5.0 of 10

    EquiEdit balances temporal consistency and editability in diffusion-based text-guided video editing via a temporal Mamba module and spectral noise injection on initial latents.

  8. Follow-Your-Creation: Empowering 4D Creation through Video Inpainting

    cs.CV 2025-06 conditional novelty 5.0 of 10

    Follow-Your-Creation fine-tunes the Wan2.1 video inpainting model on composite point-cloud and editing masks so a single monocular video can be converted into editable 4D video with new camera motion.

  9. From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence

    cs.RO 2026-07 conditional novelty 4.0 of 10

    Physical intelligence needs an embodied brain that reasons over interventions and emits capability requests, grounded by a physical harness and shared experience contracts rather than direct actuator policies.

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