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---
license: apache-2.0
library_name: MoG
---
# MoG: Motion-Aware Generative Frame Interpolation
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MoG is a generative video frame interpolation (VFI) model, designed to synthesize intermediate frames between two input frames.
MoG is the first VFI framework to bridge the gap between flow-based stability and generative flexibility. We introduce a dual-level guidance injection design to constrain generated motion using motion trajectories derived from optical flow. To enhance the generative model's ability to dynamically correct flow errors, we implement encoder-only guidance injection and selective parameter fine-tuning. As a result, MoG achieves significant improvements over existing open-source generative VFI methods, delivering superior performance in both real-world and animated scenarios.
Source code is available at [https://github.com/MCG-NJU/MoG-VFI](https://github.com/MCG-NJU/MoG-VFI).
## Network Arichitecture
## Model Description
- **Developed by:** Nanjing University, Tencent PCG
- **Model type:** Generative video frame interploation model, takes two still video frames as input.
- **Arxiv paper**: [https://arxiv.org/pdf/2501.03699](https://arxiv.org/pdf/2501.03699)
- **Project page:** [https://mcg-nju.github.io/MoG_Web/](https://mcg-nju.github.io/MoG_Web/)
- **Repository**: [https://github.com/MCG-NJU/MoG-VFI](https://github.com/MCG-NJU/MoG-VFI)
- **License:** Apache 2.0 license.
# Usage
We provide two model checkpoints: `real.ckpt` for real-world scenes and `ani.ckpt` for animation scenes. For detailed instructions on loading the checkpoints and performing inference, please refer to our [official repository](https://github.com/MCG-NJU/MoG-VFI).
## Citation
If you find our code useful or our work relevant, please consider citing:
```
@article{zhang2025motion,
title={Motion-Aware Generative Frame Interpolation},
author={Zhang, Guozhen and Zhu, Yuhan and Cui, Yutao and Zhao, Xiaotong and Ma, Kai and Wang, Limin},
journal={arXiv preprint arXiv:2501.03699},
year={2025}
}
``` |