ALANET: Adaptive Latent Attention Network for Joint Video Deblurring and Interpolation

Implementation of our paper titled "ALANET: Adaptive Latent Attention Network for Joint Video Deblurring and Interpolation" accepted to ACM-MM 2020. Please refer project page for more details. This is old version of the code. Some files may be outdated. I have also attached one checkpoint for the model.

Dataset Processing

Download data video dataset and copy it to './dataset/ ' directory.

Create Dataset

Run the following script to generate data that can be used for training and testing.

cd data

python create_dataset.py --ffmpeg_dir <path-to-ffmpeg-dir>            \
                         --dataset_folder <path-to-store-video-data>  \
                         --videos_folder ./dataset

Training ALANET

Execute run.sh bash script to train the network.

NOTE: You will have to specify parameter before running run.sh script. For more details look at run.sh in the provided codes.

Citation

@inproceedings{gupta2020alanet,
  title={ALANET: Adaptive Latent Attention Network for Joint Video Deblurring and Interpolation},
  author={Gupta, Akash and Aich, Abhishek and Roy-Chowdhury, Amit K},
  booktitle={Proceedings of the 28th ACM International Conference on Multimedia},
  pages={256--264},
  year={2020}
}

Contact

Please contact the first author Akash Gupta (agupt013@ucr.edu) for any questions.