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MotionBridge: Dynamic Video Inbetweening with Flexible Controls

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arxiv 2412.13190 v3 pith:QEGDYGN5 submitted 2024-12-17 cs.CV

classification cs.CV
keywords videocontrolsinbetweeningcontroldynamicflexibleframesframework
verification ladder T0 review T1 audit T2 compute T3 formal
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By generating plausible and smooth transitions between two image frames, video inbetweening is an essential tool for video editing and long video synthesis. Traditional works lack the capability to generate complex large motions. While recent video generation techniques are powerful in creating high-quality results, they often lack fine control over the details of intermediate frames, which can lead to results that do not align with the creative mind. We introduce MotionBridge, a unified video inbetweening framework that allows flexible controls, including trajectory strokes, keyframes, masks, guide pixels, and text. However, learning such multi-modal controls in a unified framework is a challenging task. We thus design two generators to extract the control signal faithfully and encode feature through dual-branch embedders to resolve ambiguities. We further introduce a curriculum training strategy to smoothly learn various controls. Extensive qualitative and quantitative experiments have demonstrated that such multi-modal controls enable a more dynamic, customizable, and contextually accurate visual narrative.

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Cited by 3 Pith papers

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

  1. EF-VI: Enhancing End-Frame Injection for Video Inbetweening

    cs.CV 2025-05 conditional novelty 6.0 of 10

    EF-VI injects temporally expanded end-frame features into a transformer-based image-to-video diffusion model, improving video inbetweening quality over direct fine-tuning and bidirectional sampling baselines.

  2. IKMo: Image-Keyframed Motion Generation with Trajectory-Pose Conditioned Motion Diffusion Model

    cs.GR 2025-05 conditional novelty 6.0 of 10

    A motion diffusion model with decoupled trajectory and keyframe-pose control, wrapped in an MLLM agent system, produces more controllable 3D human motion from images and text.

  3. ATI: Any Trajectory Instruction for Controllable Video Generation

    cs.CV 2025-05 conditional novelty 5.0 of 10

    ATI injects user-drawn point trajectories as soft Gaussian feature masks into a pretrained image-to-video diffusion model, enabling unified camera, object, and local motion control.

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