Deep Correlations for Texture synthesis

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Omry Sendik and Daniel Cohen-Or

Tel Aviv University

Example-based texture synthesis has been an active research problem for over two decades. Still, synthesizing textures with non-local structures remains a challenge. In this paper, we present a texture synthesis technique that builds upon convolutional neural networks and extracted statistics of pre-trained deep features. We introduce a structural energy, based on correlations among deep features, which capture the self-similarities and regularities characterizing the texture. Specifically, we show that our technique can synthesize textures that have structures of various scales, local and non-local, and the combination of the two.

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  title={Deep Correlations for Texture synthesis},

    author={Sendik, Omry and Cohen-Or, Daniel},

  journal={ACM Transactions on graphics (TOG)},

  year={2017} }