Deep Correlations for Texture synthesis
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.