Papers › A Style-Aware Content Loss for Real-time HD Style Transfer

A Style-Aware Content Loss for Real-time HD Style Transfer

26 Jul 2018ECCV 2018 9arXiv:1807.10201archive 2025-07-28

Artsiom Sanakoyeu, Dmytro Kotovenko, Sabine Lang, Björn Ommer

Recently, style transfer has received a lot of attention. While much of this research has aimed at speeding up processing, the approaches are still lacking from a principled, art historical standpoint: a style is more than just a single image or an artist, but previous work is limited to only a single instance of a style or shows no benefit from more images. Moreover, previous work has relied on a direct comparison of art in the domain of RGB images or on CNNs pre-trained on ImageNet, which requires millions of labeled object bounding boxes and can introduce an extra bias, since it has been assembled without artistic consideration. To circumvent these issues, we propose a style-aware content loss, which is trained jointly with a deep encoder-decoder network for real-time, high-resolution stylization of images and videos. We propose a quantitative measure for evaluating the quality of a stylized image and also have art historians rank patches from our approach against those from previous work. These and our qualitative results ranging from small image patches to megapixel stylistic images and videos show that our approach better captures the subtle nature in which a style affects content.

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Code

CompVis/adaptive-style-transfer mentioned on GitHubtf report
Net-Mist/style-transfer-tf2 mentioned on GitHubtfMIT report
Tonyhuiii/color-transform mentioned on GitHubpytorch report
Uemuet/style-transfer mentioned on GitHubtf report
Uemuet/styletransfer-adaptive mentioned on GitHubtf report
cristinecosta/CompVis mentioned on GitHubtf report
sundogai/style-transfer mentioned on GitHubtf report

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DecoderImage StylizationStyle TransferVideo Style Transfer

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