Code for NeurIPS 2020 paper: Blind Video Temporal Consistency via Deep Video Prior

deep-video-prior (DVP)

Code for NeurIPS 2020 paper: Blind Video Temporal Consistency via Deep Video Prior

paper | project website

Dependencey

Environment

This code is based on tensorflow. It has been tested on Ubuntu 18.04 LTS.

Anaconda is recommended: Ubuntu 18.04 | Ubuntu 16.04

After installing Anaconda, you can setup the environment simply by

conda env create -f environment.yml

Download VGG model

python download_VGG.py

Inference

Demo

bash tesh.sh

The results are placed in ./result

Use your own data

For the video with unimodal inconsistency:

python main_IRT.py --max_epoch 25 --input PATH_TO_YOUR_INPUT_FOLDER --processed PATH_TO_YOUR_PROCESSED_FOLDER --model NAME_OF_YOUR_MODEL --with_IRT 0 --IRT_initialization 0 --output ./result/OWN_DATA

For the video with multimodal inconsistency:

python main_IRT.py --max_epoch 25 --input PATH_TO_YOUR_INPUT_FOLDER --processed PATH_TO_YOUR_PROCESSED_FOLDER --model NAME_OF_YOUR_MODEL --with_IRT 1 --IRT_initialization 1 --output ./result/OWN_DATA

Citation

If you find this work useful for your research, please cite:

@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}
}                

Contact

Please contact me if there is any question (Chenyang Lei, leichenyang7@gmail.com)