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

Oriel Frigo, Neus Sabater, Julie Delon, Pierre Hellier; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 553-561

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 method based on an adaptive patch 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 our method outperforms the most recent techniques.

Related Material


[pdf]
[bibtex]
@InProceedings{Frigo_2016_CVPR,
author = {Frigo, Oriel and Sabater, Neus and Delon, Julie and Hellier, Pierre},
title = {Split and Match: Example-Based Adaptive Patch Sampling for Unsupervised Style Transfer},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}