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license: apache-2.0
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license: apache-2.0
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---
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# VFIMamba: Video Frame Interpolation with State Space Models
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This is the official checkpoint library for [VFIMamba: Video Frame Interpolation with State Space Models](https://arxiv.org/abs/2407.02315).
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Please refer to [this repository](https://github.com/MCG-NJU/VFIMamba) for our code.
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## Model Description
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VFIMamba is the first approach to adapt the SSM model to the video frame interpolation task.
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1. We devise the Mixed-SSM Block (MSB) for efficient inter-frame modeling using S6.
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2. We explore various rearrangement methods to convert two frames into a sequence, discovering that interleaved rearrangement is more suitable for VFI tasks.
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3. We propose a curriculum learning strategy to further leverage the potential of the S6 model.
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Experimental results demonstrate that VFIMamba achieves the state-of-the-art performance across various datasets, in particular highlighting the potential of the SSM model for VFI tasks with high resolution.
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## Usage
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We provide two models, an efficient version (VFIMamba-S) and a stronger one (VFIMamba). You can choose what you need by specifying the parameter model.
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### Manually Load
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Please refer to [the instruction here](https://github.com/MCG-NJU/VFIMamba/tree/main?tab=readme-ov-file#sunglassesplay-with-demos) for manually loading the checkpoints and a more customized experience.
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```bash
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python demo_2x.py --model **model[VFIMamba_S/VFIMamba]** # for 2x interpolation
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python demo_Nx.py --n 8 --model **model[VFIMamba_S/VFIMamba]** # for 8x interpolation
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```
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### Hugging Face Demo
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For Hugging Face demo, please refer to [the code here](https://github.com/MCG-NJU/VFIMamba/blob/main/hf_demo_2x.py).
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```bash
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python hf_demo_2x.py --model **model[VFIMamba_S/VFIMamba]** # for 2x interpolation
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```
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## Citation
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If you think this project is helpful in your research or for application, please feel free to leave a star⭐️ and cite our paper:
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```
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@misc{zhang2024vfimambavideoframeinterpolation,
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title={VFIMamba: Video Frame Interpolation with State Space Models},
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author={Guozhen Zhang and Chunxu Liu and Yutao Cui and Xiaotong Zhao and Kai Ma and Limin Wang},
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year={2024},
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eprint={2407.02315},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2407.02315},
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}
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```
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