%0 Generic %1 citeulike:14553960 %A xxx, %D 2018 %K deconv metric semisup style\_transfer %T A Closed-form Solution to Photorealistic Image Stylization %U http://arxiv.org/abs/1802.06474 %X Photorealistic image style transfer algorithms aim at stylizing a content photo using the style of a reference photo with the constraint that the stylized photo should remains photorealistic. While several methods exist for this task, they tend to generate spatially inconsistent stylizations with noticeable artifacts. In addition, these methods are computationally expensive, requiring several minutes to stylize a VGA photo. In this paper, we present a novel algorithm to address the limitations. The proposed algorithm consists of a stylization step and a smoothing step. While the stylization step transfers the style of the reference photo to the content photo, the smoothing step encourages spatially consistent stylizations. Unlike existing algorithms that require iterative optimization, both steps in our algorithm have closed-form solutions. Experimental results show that the stylized photos generated by our algorithm are twice more preferred by human subjects in average. Moreover, our method runs 60 times faster than the state-of-the-art approach. Code and additional results are available at <a href="https://github.com/NVIDIA/FastPhotoStyle">this https URL</a> .

@misc{citeulike:14553960, abstract = {{Photorealistic image style transfer algorithms aim at stylizing a content photo using the style of a reference photo with the constraint that the stylized photo should remains photorealistic. While several methods exist for this task, they tend to generate spatially inconsistent stylizations with noticeable artifacts. In addition, these methods are computationally expensive, requiring several minutes to stylize a VGA photo. In this paper, we present a novel algorithm to address the limitations. The proposed algorithm consists of a stylization step and a smoothing step. While the stylization step transfers the style of the reference photo to the content photo, the smoothing step encourages spatially consistent stylizations. Unlike existing algorithms that require iterative optimization, both steps in our algorithm have closed-form solutions. Experimental results show that the stylized photos generated by our algorithm are twice more preferred by human subjects in average. Moreover, our method runs 60 times faster than the state-of-the-art approach. Code and additional results are available at <a href="https://github.com/NVIDIA/FastPhotoStyle">this https URL</a> .}}, added-at = {2019-02-27T22:23:29.000+0100}, archiveprefix = {arXiv}, author = {xxx}, biburl = {https://www.bibsonomy.org/bibtex/20aea4e00051ae492e77395551cfe8a1e/nmatsuk}, citeulike-article-id = {14553960}, citeulike-linkout-0 = {http://arxiv.org/abs/1802.06474}, citeulike-linkout-1 = {http://arxiv.org/pdf/1802.06474}, day = 22, eprint = {1802.06474}, interhash = {a23bee8fb98556b320c107f24547c63b}, intrahash = {0aea4e00051ae492e77395551cfe8a1e}, keywords = {deconv metric semisup style\_transfer}, month = feb, posted-at = {2018-03-22 07:41:14}, priority = {0}, timestamp = {2019-02-27T22:23:29.000+0100}, title = {{A Closed-form Solution to Photorealistic Image Stylization}}, url = {http://arxiv.org/abs/1802.06474}, year = 2018 }