--- license: apache-2.0 library_name: vfi-mamba --- # VFIMamba: Video Frame Interpolation with State Space Models This is the official checkpoint library for [VFIMamba: Video Frame Interpolation with State Space Models](https://arxiv.org/abs/2407.02315). Please refer to [this repository](https://github.com/MCG-NJU/VFIMamba) for our code. ## Model Description VFIMamba is the first approach to adapt the SSM model to the video frame interpolation task. 1. We devise the Mixed-SSM Block (MSB) for efficient inter-frame modeling using S6. 2. We explore various rearrangement methods to convert two frames into a sequence, discovering that interleaved rearrangement is more suitable for VFI tasks. 3. We propose a curriculum learning strategy to further leverage the potential of the S6 model. 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. ## Usage 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. ### Manually Load 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. ```bash python demo_2x.py --model **model[VFIMamba_S/VFIMamba]** # for 2x interpolation python demo_Nx.py --n 8 --model **model[VFIMamba_S/VFIMamba]** # for 8x interpolation ``` ### Hugging Face Demo For Hugging Face demo, please refer to [the code here](https://github.com/MCG-NJU/VFIMamba/blob/main/hf_demo_2x.py). ```bash python hf_demo_2x.py --model **model[VFIMamba_S/VFIMamba]** # for 2x interpolation ``` ## Citation If you think this project is helpful in your research or for application, please feel free to leave a star⭐️ and cite our paper: ``` @misc{zhang2024vfimambavideoframeinterpolation, title={VFIMamba: Video Frame Interpolation with State Space Models}, author={Guozhen Zhang and Chunxu Liu and Yutao Cui and Xiaotong Zhao and Kai Ma and Limin Wang}, year={2024}, eprint={2407.02315}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2407.02315}, } ```