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Recent style transfer models have provided promising artistic results.However, given a photograph as a reference style, existing methods are limitedby spatial distortions or unrealistic artifacts, which should not happen inreal photographs. We introduce a theoretically sound correction to the networkarchitecture that remarkably enhances photorealism and faithfully transfers thestyle. The key ingredient of our method is wavelet transforms that naturallyfits in deep networks. We propose a wavelet corrected transfer based onwhitening and coloring transforms (WCT$^2$) that allows features to preservetheir structural information and statistical properties of VGG feature spaceduring stylization. This is the first and the only end-to-end model that canstylize a $1024\times1024$ resolution image in 4.7 seconds, giving a pleasingand photorealistic quality without any post-processing. Last but not least, ourmodel provides a stable video stylization without temporal constraints. Ourcode, generated images, and pre-trained models are all available athttps://github.com/ClovaAI/WCT2.

Photorealistic Style Transfer via Wavelet Transforms