Photo blending is a common technique to create aesthetically pleasing artworks by combining multiple photos. However, the process of photo blending is usually time-consuming, and care must be taken in the process of blending, filtering, positioning, and masking each of the source photos. To make photo blending accessible to general public, we propose an efficient approach for automatic photo blending via deep learning. Specifically, given a foreground image and a background image, our proposed method automatically generates a set of blending photos with scores that indicate the aesthetics quality with the proposed quality network and policy network. Experimental results show that the proposed approach can effectively generate high quality blending photos with efficiency.
|Title of host publication||Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings|
|Editors||Vittorio Ferrari, Cristian Sminchisescu, Martial Hebert, Yair Weiss|
|Number of pages||17|
|Publication status||Published - 2018|
|Event||15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany|
Duration: 2018 Sep 8 → 2018 Sep 14
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||15th European Conference on Computer Vision, ECCV 2018|
|Period||18/9/8 → 18/9/14|
Bibliographical noteFunding Information:
Acknowledgments. This work is supported in part by the NSF CAREER Grant #1149783, gifts from Adobe and NVIDIA.
© Springer Nature Switzerland AG 2018.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)