conda env create -f environment.yml
conda activate deep-video-prior

cd deep-video-prior
python download_VGG.py
unzip VGG_Model.zip

bash test.sh

python dvp_video_consistency.py --input PATH_TO_YOUR_INPUT_FOLDER --processed PATH_TO_YOUR_PROCESSED_FOLDER --task NAME_OF_YOUR_MODEL  --output ./result/OWN_DATA

python dvp_video_consistency.py --input PATH_TO_YOUR_INPUT_FOLDER --processed PATH_TO_YOUR_PROCESSED_FOLDER --task NAME_OF_YOUR_MODEL --with_IRT 1 --IRT_initialization 1 --output ./result/OWN_DATA

  -h, --help            show this help message and exit
  --task TASK           Name of task
  --input INPUT         Dir of input video
  --processed PROCESSED
                        Dir of processed video
  --output OUTPUT       Dir of output video
  --use_gpu USE_GPU     Use gpu or not
  --loss {perceptual,l1,l2}
                        Chooses which loss to use. perceptual, l1, l2
  --network {unet}      Chooses which model to use. unet, fcn
  --coarse_to_fine_speedup COARSE_TO_FINE_SPEEDUP
                        Use coarse_to_fine_speedup for training
  --with_IRT WITH_IRT   Sse IRT or not, set this to 1 if you want to solve
                        multimodal inconsistency
  --IRT_initialization IRT_INITIALIZATION
                        Sse initialization for IRT
  --large_video LARGE_VIDEO
                        Set this to 1 when the number of video frames are
                        large, e.g., more than 1000 frames
  --save_freq SAVE_FREQ
                        Save frequency of epochs
  --max_epoch MAX_EPOCH
                        The max number of epochs for training
  --format FORMAT       Format of output image

@inproceedings{lei2020dvp,
  title={Blind Video Temporal Consistency via Deep Video Prior},
  author={Lei, Chenyang and Xing, Yazhou and Chen, Qifeng},
  booktitle={Advances in Neural Information Processing Systems},
  year={2020}
}