Exploring the structure of a real-time, arbitrary neural artistic stylization network
Abstract
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.
Cite
@article{arxiv.1705.06830,
title = {Exploring the structure of a real-time, arbitrary neural artistic stylization network},
author = {Golnaz Ghiasi and Honglak Lee and Manjunath Kudlur and Vincent Dumoulin and Jonathon Shlens},
journal= {arXiv preprint arXiv:1705.06830},
year = {2017}
}
Comments
Accepted as an oral presentation at British Machine Vision Conference (BMVC) 2017