Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer

Tianwei Lin, Zhuoqi Ma, Fu Li, Dongliang He, Xin Li, Errui Ding, Nannan Wang, Jie Li, Xinbo Gao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 5141-5150

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 Laplacian Pyramid Network (LapStyle). LapStyle first transfers global style pattern 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.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Lin_2021_CVPR, author = {Lin, Tianwei and Ma, Zhuoqi and Li, Fu and He, Dongliang and Li, Xin and Ding, Errui and Wang, Nannan and Li, Jie and Gao, Xinbo}, title = {Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, pages = {5141-5150} }