Learning Blind Video Temporal Consistency (ECCV 2018)

Wei-Sheng Lai, Jia-Bin Huang, Oliver Wang, Eli Shechtman, Ersin Yumer, and Ming-Hsuan Yang

European Conference on Computer Vision (ECCV), 2018

[Project page][Paper]

Table of Contents

1. Introduction
2. Citation
3. Requirements and Dependencies
4. Installation
5. Dataset
6. Apply Pre-trained Models
7. Training and Testing
8. Image processing algorithms

Introduction

Our method takes the original unprocessed and per-frame processed videos as inputs to produce a temporally consistent video. Our approach is agnostic to specific image processing algorithms applied on the original video.

Citation

If you find the code and datasets useful in your research, please cite:

@inproceedings{Lai-ECCV-2018,
    author    = {Lai, Wei-Sheng and Huang, Jia-Bin and Wang, Oliver and Shechtman, Eli and Yumer, Ersin and Yang, Ming-Hsuan}, 
    title     = {Learning Blind Video Temporal Consistency}, 
    booktitle = {European Conference on Computer Vision},
    year      = {2018}
}

Requirements and dependencies

• Pytorch 0.4
• TensorboardX
• LPIPS (for evaluation)

Dataset

Installation

Download repository:

$ git clone https://github.com/phoenix104104/fast_blind_video_consistency.git

Apply pre-trained models

Training and testing

Image processing algorithms

We use the following algorithms to obtain per-frame processed results:

Style transfer