Split and Match: Example-based Adaptive Patch Sampling for Unsupervised Style Transfer
Oriel Frigo (University Paris Descartes / Technicolor),
Neus Sabater (Technicolor)
Julie Delon (University Paris Descartes),
Pierre Hellier (Technicolor)
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.
Downloads:
Some results:
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Example 1: Van Gogh's Starry Night | Example 2: Seurat's Woman |
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| Input 1: Cat image | Input 1, stylized by Example 1 | Input 1, stylized by Example 2 |
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| Input 2: Venice image | Input 2, stylized by Example 1 | Input 2, stylized by Example 2 |
