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• W2922908764 abstract "Recent style transfer models have provided promising artistic results. However, given a photograph as a reference style, existing methods are limited by spatial distortions or unrealistic artifacts, which should not happen in real photographs. We introduce a theoretically sound correction to the network architecture that remarkably enhances photorealism and faithfully transfers the style. The key ingredient of our method is wavelet transforms that naturally fits in deep networks. We propose a wavelet corrected transfer based on whitening and coloring transforms (WCT$^2$) that allows features to preserve their structural information and statistical properties of VGG feature space during stylization. This is the first and the only end-to-end model that can stylize a $1024times1024$ resolution image in 4.7 seconds, giving a pleasing and photorealistic quality without any post-processing. Last but not least, our model provides a stable video stylization without temporal constraints. Our code, generated images, and pre-trained models are all available at https://github.com/ClovaAI/WCT2." @default.
• W2922908764 created "2019-04-01" @default.
• W2922908764 creator A5041472021 @default.
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• W2922908764 date "2019-03-23" @default.
• W2922908764 modified "2023-10-16" @default.
• W2922908764 title "Photorealistic Style Transfer via Wavelet Transforms" @default.
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• W2922908764 doi "https://doi.org/10.48550/arxiv.1903.09760" @default.
• W2922908764 hasPublicationYear "2019" @default.
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