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arXiv · 2203.13215

Neural Neighbor Style Transfer

Nicholas Kolkin·Michal Kucera·Sylvain Paris·Daniel Sykora·Eli Shechtman·Greg Shakhnarovich
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Abstract

We propose Neural Neighbor Style Transfer (NNST), a pipeline that offers state-of-the-art quality, generalization, and competitive efficiency for artistic style transfer. Our approach is based on explicitly replacing neural features extracted from the content input (to be stylized) with those from a style exemplar, then synthesizing the final output based on these rearranged features. While the spirit of our approach is similar to prior work, we show that our design decisions dramatically improve the final visual quality.

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Nicholas Kolkin, Michal Kucera, Sylvain Paris, Daniel Sykora, Eli Shechtman, Greg Shakhnarovich. 2022-03-24. Neural Neighbor Style Transfer. https://arxiv.org/abs/2203.13215

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