Title
Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer
Abstract
Artistic style transfer aims at migrating the style from an example image to a content image. Currently, optimization-based methods have achieved great stylization quality, but expensive time cost restricts their practical applications. Meanwhile, feed-forward methods still fail to synthesize complex style, especially when holistic global and local patterns exist. Inspired by the common painting process of drawing a draft and revising the details, we introduce a novel feed-forward method named Laplacian Pyramid Network (LapStyle). LapStyle first transfers global style patterns in low-resolution via a Drafting Network. It then revises the local details in high-resolution via a Revision Network, which hallucinates a residual image according to the draft and the image textures extracted by Laplacian filtering. Higher resolution details can be easily generated by stacking Revision Networks with multiple Laplacian pyramid levels. The final stylized image is obtained by aggregating outputs of all pyramid levels. Experiments demonstrate that our method can synthesize high quality stylized images in real time, where holistic style patterns are properly transferred.
Year
DOI
Venue
2021
10.1109/CVPR46437.2021.00510
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
DocType
ISSN
Citations 
Conference
1063-6919
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Tianwei Lin1546.67
Zhuoqi Ma231.72
Fu Li332.42
He, D.43313.67
Xin Li532.74
Er-rui Ding614229.31
Nannan Wang770750.01
Jie Li81266116.12
Xinbo Gao95534344.56