Split and Match: Example-based Adaptive Patch Sampling for Unsupervised Style Transfer

Abstract : This paper presents a novel unsupervised method to transfer the style of an example image to a source image. The complex notion of image style is here considered as a local texture transfer, eventually coupled with a global color transfer. For the local texture transfer, we propose a new patch-based method based on an adaptive partition that captures the style of the example image and preserves the structure of the source image. More precisely, this example-based partition predicts how well a source patch matches an example patch. Results on various images show that out method outperforms the most recent techniques.
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Communication dans un congrès
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2016, Las Vegas, United States. 2016, 〈http://cvpr2016.thecvf.com/〉
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Contributeur : Oriel Frigo <>
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Dernière modification le : mardi 10 octobre 2017 - 11:22:04
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  • HAL Id : hal-01280818, version 2

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Oriel Frigo, Neus Sabater, Julie Delon, Pierre Hellier. Split and Match: Example-based Adaptive Patch Sampling for Unsupervised Style Transfer. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2016, Las Vegas, United States. 2016, 〈http://cvpr2016.thecvf.com/〉. 〈hal-01280818v2〉

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