[Paper] [video]

• NVIDIA GPU + CUDA 9.0 + CuDNN 7.6.5
• Pytorch 1.1.0

First clone the project

git clone https://github.com/yjzhang96/UTI-VFI 
cd UTI-VFI
mkdir pretrain_models

Download pretrained model weights from Google Drive. Put model weights "SEframe_net.pth" and "refine_net.pth" into directory "./pretrain_models"; put "model.ckpt" and "network-default.pytorch" into directory "./utils"

download GoPro datasets with all the figh-frame-rate video frames from GOPRO_Large_all, and generate blurry videos for different exposure settings. You can generate the test datasets via run:

python utils/generate_blur.py

After prepared test datasets, you can run test usding the following command:

sh run_test.sh

Note that to test the model on GOPRO datasets (datasets with groud-truth to compare), you need to set the argument "--test_type" to ''validation''. If you want to test the model on real-world video (without ground-truth), you need to use "real_world" instead.

@inproceedings{Zhang2019video,
  title={Video Frame Interpolation without Temporal Priors},
  author={Zhang, Youjian and Wang, Chaoyue and Tao, Dacheng},
  journal={Advances in Neural Information Processing Systems},
  year={2020}
}

Code of interpolation module borrows heavily from QVI