Enhancing Neural Style Transfer using Patch-Based Synthesis

Ondřej Texler
CTU in Prague, FEE
Jakub Fišer
Adobe Research
Michal Lukáč
Adobe Research
Jingwan Lu
Adobe Research
Eli Shechtman
Adobe Research
Daniel Sýkora
CTU in Prague, FEE

Gatys et al. [2016] Style exemplar Our approach


We present a new approach to example-based style transfer which combines neural methods with patch-based synthesis to achieve compelling stylization quality even for high-resolution imagery. We take advantage of neural techniques to provide adequate stylization at the global level and use their output as a prior for subsequent patch-based synthesis at the detail level. Thanks to this combination, our method keeps the high frequencies of the original artistic media better, thereby dramatically increases the fidelity of the resulting stylized imagery. We also show how to stylize extremely large images (e.g., 340 Mpix) without the need to run the synthesis at the pixel level, yet retaining the original high-frequency details.

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Proceedings of the 8th ACM/EG Expressive Symposium, pp. 43–50, 2019

(Expressive 2019, Genoa, Italy, May 2019)

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