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-# "Learning To Blend Photos," ECCV 2018
+# Learning To Blend Photos, ECCV 2018
 
+## Code/Data will be released soon 
+
+This repo demonstrate the following paper:
+
+[Learning to Blend Photos](http://openaccess.thecvf.com/content_ECCV_2018/html/Wei-Chih_Hung_Learning_to_Blend_ECCV_2018_paper.html) <br/>
+[Wei-Chih Hung](https://hfslyc.github.io/), [Jianming Zhang](https://jimmie33.github.io/), [Xiaohui Shen](http://users.eecs.northwestern.edu/~xsh835/), [Zhe Lin](https://research.adobe.com/person/zhe-lin/), [Joon-Young Lee](https://joonyoung-cv.github.io/), and [Ming-Hsuan Yang](http://faculty.ucmerced.edu/mhyang/) <br/>
+Proceedings of the European Conference on Computer Vision (ECCV), 2018.
+
+Contact: Wei-Chih Hung (whung8 at ucmerced dot edu)
+
+# Introduction
+
+In this work, we aim to automate the photo blending process through deep reinforcement learning. We focus on a specific and popular photo blending style - Double Exposure. The image below shows some example results of our method:
+
+![](images/teaser.png)
+
+The figure below shows the overview of our system. The inputs of our method are two photos: foreground and background. We first train a quality network to evaluate the aesthetics quality of blending photos with human preference annotation on random blending photos. Then a deep reinforcement learning based agent is trained to optimize the parameter for the background alignment and photometric adjustment. Using the predicted parameters, the blending engine renders the final blending photo. 
+
+![](images/framework.png)
 
 
 Please cite our paper if you find it useful for your research.