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Motion-Zero: Zero-Shot Moving Object Control Framework for Diffusion-Based Video Generation

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arxiv 2401.10150 v4 pith:V6ZNJAE4 submitted 2024-01-18 cs.CV

Changgu Chen , Junwei Shu , Gaoqi He , Changbo Wang , Yang Li This is my paper
classification cs.CV
keywords diffusioncontrolmovingmodelsobjectobjectsvideovideos
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent large-scale pre-trained diffusion models have demonstrated a powerful generative ability to produce high-quality videos from detailed text descriptions. However, exerting control over the motion of objects in videos generated by any video diffusion model is a challenging problem. In this paper, we propose a novel zero-shot moving object trajectory control framework, Motion-Zero, to enable a bounding-box-trajectories-controlled text-to-video diffusion model. To this end, an initial noise prior module is designed to provide a position-based prior to improve the stability of the appearance of the moving object and the accuracy of position. In addition, based on the attention map of the U-net, spatial constraints are directly applied to the denoising process of diffusion models, which further ensures the positional and spatial consistency of moving objects during the inference. Furthermore, temporal consistency is guaranteed with a proposed shift temporal attention mechanism. Our method can be flexibly applied to various state-of-the-art video diffusion models without any training process. Extensive experiments demonstrate our proposed method can control the motion trajectories of objects and generate high-quality videos. Our project page is https://vpx-ecnu.github.io/MotionZero-website/

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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. Interactive Video Generation via Domain Adaptation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A training-free method combines mask normalization and temporal intrinsic denoising to improve trajectory control and perceptual quality in text-to-video diffusion.

  2. AniCrafter: Customizing Realistic Human-Centric Animation via Avatar-Background Conditioning in Video Diffusion Models

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A diffusion model animates a character into arbitrary dynamic backgrounds by conditioning on a rendered 3D-avatar video, reframing open-domain animation as a restoration problem.

  3. 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.

  4. 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.

  5. 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.

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