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

Oriel Frigo, Neus Sabater, Julie Delon, Pierre Hellier

CVPR 2016

doi:10.1109/CVPR.2016.66 /cvpr/2016/frigo2016cvpr-split/

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.

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Cite

Text

Frigo et al. "Split and Match: Example-Based Adaptive Patch Sampling for Unsupervised Style Transfer." Conference on Computer Vision and Pattern Recognition, 2016. doi:10.1109/CVPR.2016.66

Markdown

[Frigo et al. "Split and Match: Example-Based Adaptive Patch Sampling for Unsupervised Style Transfer." Conference on Computer Vision and Pattern Recognition, 2016.](https://mlanthology.org/cvpr/2016/frigo2016cvpr-split/) doi:10.1109/CVPR.2016.66

BibTeX

@inproceedings{frigo2016cvpr-split,
  title     = {{Split and Match: Example-Based Adaptive Patch Sampling for Unsupervised Style Transfer}},
  author    = {Frigo, Oriel and Sabater, Neus and Delon, Julie and Hellier, Pierre},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2016},
  doi       = {10.1109/CVPR.2016.66},
  url       = {https://mlanthology.org/cvpr/2016/frigo2016cvpr-split/}
}