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Artistic style transfer aims at migrating the style from an example image toa content image. Currently, optimization-based methods have achieved greatstylization quality, but expensive time cost restricts their practicalapplications. Meanwhile, feed-forward methods still fail to synthesize complexstyle, especially when holistic global and local patterns exist. Inspired bythe common painting process of drawing a draft and revising the details, weintroduce a novel feed-forward method named Laplacian Pyramid Network(LapStyle). LapStyle first transfers global style patterns in low-resolutionvia a Drafting Network. It then revises the local details in high-resolutionvia a Revision Network, which hallucinates a residual image according to thedraft and the image textures extracted by Laplacian filtering. Higherresolution details can be easily generated by stacking Revision Networks withmultiple Laplacian pyramid levels. The final stylized image is obtained byaggregating outputs of all pyramid levels. %We also introduce a patchdiscriminator to better learn local patterns adversarially. Experimentsdemonstrate that our method can synthesize high quality stylized images in realtime, where holistic style patterns are properly transferred.

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