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

Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin and Jonathon Shlens

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.

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DOI

10.5244/C.31.114
https://dx.doi.org/10.5244/C.31.114

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            @inproceedings{BMVC2017_114,
                title={Exploring the structure of a real-time, arbitrary neural artistic stylization network},
                author={Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin and Jonathon Shlens},
                year={2017},
                month={September},
                pages={114.1-114.12},
                articleno={114},
                numpages={12},
                booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
                publisher={BMVA Press},
                editor={Tae-Kyun Kim, Stefanos Zafeiriou, Gabriel Brostow and Krystian Mikolajczyk},
                doi={10.5244/C.31.114},
                isbn={1-901725-60-X},
                url={https://dx.doi.org/10.5244/C.31.114}
            }