# MotionBridge: Dynamic Video Inbetweening with Flexible Controls

arXiv: https://arxiv.org/abs/2412.13190 · HTML: https://mlsanity.com/paper/2412.13190/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2024.

## Built on by (most cited first, 10 of 10)

- [MotionStream: Real-Time Video Generation with Interactive Motion Controls](https://mlsanity.com/paper/2511.01266/) (2025)
- [ATI: Any Trajectory Instruction for Controllable Video Generation](https://mlsanity.com/paper/2505.22944/) (2025)
- [VerseCrafter: Dynamic Realistic Video World Model with 4D Geometric Control](https://mlsanity.com/paper/2601.05138/) (2026)
- [VidCRAFT3: Camera, Object, and Lighting Control for Image-to-Video Generation](https://mlsanity.com/paper/2502.07531/) (2025)
- [Generative AI for Cel-Animation: A Survey](https://mlsanity.com/paper/2501.06250/) (2025)
- [Motion-Aware Generative Frame Interpolation](https://mlsanity.com/paper/2501.03699/) (2025)
- [Frame In-N-Out: Unbounded Controllable Image-to-Video Generation](https://mlsanity.com/paper/2505.21491/) (2025)
- [PhyCo: Learning Controllable Physical Priors for Generative Motion](https://mlsanity.com/paper/2604.28169/) (2026)
- [EF-VI: Enhancing End-Frame Injection for Video Inbetweening](https://mlsanity.com/paper/2505.21205/) (2025)
- [Mobius: Text to Seamless Looping Video Generation via Latent Shift](https://mlsanity.com/paper/2502.20307/) (2025)

## Abstract (arXiv)

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.