Exploring the structure of a real-time, arbitrary neural artistic stylization network

In this paper, we present a method which combines the flexibility of the neural algorithm of artistic style with the speed of fast style transfer networks to allow real-time stylization using any content/style image pair. We build upon recent work leveraging conditional instance normalization for multi-style transfer networks by learning to predict the conditional instance normalization parameters directly from a style image. The model is successfully trained on a corpus of roughly 80,000 paintings and is able to generalize to paintings previously unobserved. We demonstrate that the learned embedding space is smooth and contains a rich structure and organizes semantic information associated with paintings in an entirely unsupervised manner.

• Publication year

  2017

• Venue

  British Machine Vision Conference

• Publication date

  2017-05-18

• Fields of study

  Art, Computer Science

• Identifiers
  DOI 10.5244/C.31.114arXiv 1705.06830
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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