Official PyTorch implementation of our CVPR2022 paper Video Frame Interpolation with Transformer

• [08/02/2023] Thank @jhogsett for creating the WebUI for easier use!

• python >= 3.8
• pytorch >= 1.8.0
• torchvision >= 0.9.0

1. Vimeo90K Triplet dataset
2. MiddleBury Other dataset
3. UCF101 dataset
4. SNU-FILM dataset

To train on the Vimeo90K, we have to first compute the ground-truth flows between frames using Lite-flownet, you can clone the Lite-flownet repo and put compute_flow_vimeo.py we provide under its main directory and run (remember to change the data path in these lines, the liteflownet checkpoint in this line can be found here):

python compute_flow_vimeo.py

1. Clone this repo.

   git clone https://github.com/Jia-Research-Lab/VFIformer.git
   cd VFIformer
   

2. Modify the argument --data_root in train.py according to your Vimeo90K path.

1. Download the pre-trained models and place them into the pretrained_models/ folder.

   • Pre-trained models can be downloaded from Google Drive
     • pretrained_VFIformer: the final model in the main paper
     • pretrained_VFIformerSmall: the smaller version of the model mentioned in the supplementary file

2. Test on the Vimeo90K testing set.

   Modify the argument --data_root according to your data path, run:

   python test.py --data_root [your Vimeo90K path] --testset VimeoDataset --net_name VFIformer --resume ./pretrained_models/pretrained_VFIformer/net_220.pth --save_result
   

   If you want to test with the smaller model, please change the --net_name and --resume accordingly:

   python test.py --data_root [your Vimeo90K path] --testset VimeoDataset --net_name VFIformerSmall --resume ./pretrained_models/pretrained_VFIformerSmall/net_220.pth --save_result
   

   The testing results are saved in the test_results/ folder. If you do not want to save the image results, you can remove the --save_result argument in the commands optionally.

3. Test on the MiddleBury dataset.

   Modify the argument --data_root according to your data path, run:

   python test.py --data_root [your MiddleBury path] --testset MiddleburyDataset --net_name VFIformer --resume ./pretrained_models/pretrained_VFIformer/net_220.pth --save_result
   

4. Test on the UCF101 dataset.

   Modify the argument --data_root according to your data path, run:

   python test.py --data_root [your UCF101 path] --testset UFC101Dataset --net_name VFIformer --resume ./pretrained_models/pretrained_VFIformer/net_220.pth --save_result
   

5. Test on the SNU-FILM dataset.

   Modify the argument --data_root according to your data path. Choose the motion level and modify the argument --test_level accordingly, run:

   python FILM_test.py --data_root [your SNU-FILM path] --test_level [easy/medium/hard/extreme] --net_name VFIformer --resume ./pretrained_models/pretrained_VFIformer/net_220.pth
   

1. First train the flow estimator. (Note that skipping this step will not cause a significant impact on performance. We keep this step here only to be consistent with our paper.)

   python -m torch.distributed.launch --nproc_per_node=4 --master_port=4174 train.py --launcher pytorch --gpu_ids 0,1,2,3 \
           --loss_flow --use_tb_logger --batch_size 48 --net_name IFNet --name train_IFNet --max_iter 300 --crop_size 192 --save_epoch_freq 5
   

2. Then train the whole framework.

   python -m torch.distributed.launch --nproc_per_node=8 --master_port=4175 train.py --launcher pytorch --gpu_ids 0,1,2,3,4,5,6,7 \
           --loss_l1 --loss_ter --loss_flow --use_tb_logger --batch_size 24 --net_name VFIformer --name train_VFIformer --max_iter 300 \
           --crop_size 192 --save_epoch_freq 5 --resume_flownet ./weights/train_IFNet/snapshot/net_final.pth
   

3. To train the smaller version, run:

   python -m torch.distributed.launch --nproc_per_node=8 --master_port=4175 train.py --launcher pytorch --gpu_ids 0,1,2,3,4,5,6,7 \
           --loss_l1 --loss_ter --loss_flow --use_tb_logger --batch_size 24 --net_name VFIformerSmall --name train_VFIformerSmall --max_iter 300 \
           --crop_size 192 --save_epoch_freq 5 --resume_flownet ./weights/train_IFNet/snapshot/net_final.pth
   

1. Modify the arguments --img0_path and --img1_path according to your data path, run:

   python demo.py --img0_path [your img0 path] --img1_path [your img1 path] --save_folder [your save path] --net_name VFIformer --resume ./pretrained_models/pretrained_VFIformer/net_220.pth
   

We borrow some codes from RIFE and SwinIR. We thank the authors for their great work.

Please consider citing our paper in your publications if it is useful for your research.

@inproceedings{lu2022vfiformer,
    title={Video Frame Interpolation with Transformer},
    author={Liying Lu, Ruizheng Wu, Huaijia Lin, Jiangbo Lu, and Jiaya Jia},
    booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2022},
}

lylu@cse.cuhk.edu.hk