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MotionMaster: Training-free Camera Motion Transfer For Video Generation

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arxiv 2404.15789 v2 pith:G7TUOSL3 submitted 2024-04-24 cs.CV

Teng Hu , Jiangning Zhang , Ran Yi , Yating Wang , Hongrui Huang , Jieyu Weng , Yabiao Wang , Lizhuang Ma This is my paper
classification cs.CV
keywords cameramotionvideocontrolgenerationmotionsproposevideos
verification ladder T0 review T1 audit T2 compute T3 formal
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The emergence of diffusion models has greatly propelled the progress in image and video generation. Recently, some efforts have been made in controllable video generation, including text-to-video generation and video motion control, among which camera motion control is an important topic. However, existing camera motion control methods rely on training a temporal camera module, and necessitate substantial computation resources due to the large amount of parameters in video generation models. Moreover, existing methods pre-define camera motion types during training, which limits their flexibility in camera control. Therefore, to reduce training costs and achieve flexible camera control, we propose COMD, a novel training-free video motion transfer model, which disentangles camera motions and object motions in source videos and transfers the extracted camera motions to new videos. We first propose a one-shot camera motion disentanglement method to extract camera motion from a single source video, which separates the moving objects from the background and estimates the camera motion in the moving objects region based on the motion in the background by solving a Poisson equation. Furthermore, we propose a few-shot camera motion disentanglement method to extract the common camera motion from multiple videos with similar camera motions, which employs a window-based clustering technique to extract the common features in temporal attention maps of multiple videos. Finally, we propose a motion combination method to combine different types of camera motions together, enabling our model a more controllable and flexible camera control. Extensive experiments demonstrate that our training-free approach can effectively decouple camera-object motion and apply the decoupled camera motion to a wide range of controllable video generation tasks, achieving flexible and diverse camera motion control.

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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. UniMoCa: Unifying Motion and Camera Controls as Visual Proxies for Faithful Human Video Generation

    cs.CV 2026-08 conditional novelty 6.0 of 10

    A visual proxy that renders human motion under the driving camera and overlays camera trajectory markers lets a video diffusion model control both body motion and camera movement from a single visual conditioning space.

  2. TARS: Timestep-Aware Data Scaling for 3D-Free Video Re-Shooting

    cs.CV 2026-07 conditional novelty 6.0 of 10

    TARS splits videos into clip pairs for self-supervised camera learning, adds text-driven viewpoint labels, and restricts scarce paired training to high-noise timesteps.

  3. 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.

  4. 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.

  5. PolyVivid: Vivid Multi-Subject Video Generation with Cross-Modal Interaction and Enhancement

    cs.CV 2025-06 conditional novelty 6.0 of 10

    PolyVivid combines VLLM-based grounding, 3D-RoPE positional encoding, and attention-inherited identity injection to generate customized videos with multiple consistent subjects and text-specified interactions.

  6. LiON-LoRA: Rethinking LoRA Fusion to Unify Controllable Spatial and Temporal Generation for Video Diffusion

    cs.CV 2025-07 conditional novelty 5.0 of 10

    LiON-LoRA adds a learned scaling token to video-diffusion LoRA adapters, enabling linear and independent control of camera trajectory and object motion strength.

  7. RealCam-I2V: Real-World Image-to-Video Generation with Interactive Complex Camera Control

    cs.CV 2025-02 conditional novelty 5.0 of 10

    Metric-scale depth alignment plus scene-constrained noise shaping improves camera controllability and video quality for image-to-video generation on RealEstate10K.

  8. 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.

  9. Camera Trajectory Generation: A Comprehensive Survey of Methods, Metrics, and Future Directions

    cs.CV 2025-06 conditional novelty 4.0 of 10

    A review that organizes camera trajectory generation into representation levels, algorithm families, evaluation metrics, and datasets.

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