This repo is the official implementation of 'Video Frame Interpolation Transformer', CVPR 2022.

Paper, Video, Video without compression

The following pakages are required to run the code:

• python==3.7.6
• pytorch==1.5.1
• cudatoolkit==10.1
• torchvision==0.6.1
• cupy==7.5.0
• pillow==8.2.0
• einops==0.3.0

• Download the Vimeo-90K septuplets dataset.
• Then train VFIT-B using default training configurations:

python main.py --model VFIT_B --dataset vimeo90K_septuplet --data_root <dataset_path>

Training VFIT-S is similiar to above, just change model to VFIT_S.

After training, you can evaluate the model with following command:

python test.py --model VFIT_B --dataset vimeo90K_septuplet --data_root <dataset_path> --load_from checkpoints/model_best.pth

You can also evaluate VFIT using our weight here.

More datasets for evaluation:

• UCF
• Davis

Please consider citing this paper if you find the code and data useful in your research:

@inproceedings{shi2022video,
  title={Video Frame Interpolation Transformer},
  author={Shi, Zhihao and Xu, Xiangyu and Liu, Xiaohong and Chen, Jun and Yang, Ming-Hsuan},
  booktitle={CVPR},
  year={2022}
}

Some other great video interpolation resources that we benefit from:

• FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation, arXiv 2021 Code
• QVI: Quadratic Video Interpolation, NeurIPS 2019 Code
• AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation, CVPR 2020 Code