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.
@article{arxiv.2203.13215,
title = {Neural Neighbor Style Transfer},
author = {Nicholas Kolkin and Michal Kucera and Sylvain Paris and Daniel Sykora and Eli Shechtman and Greg Shakhnarovich},
journal= {arXiv preprint arXiv:2203.13215},
year = {2022}
}
Comments
Code for NNST-Opt available at https://github.com/nkolkin13/NeuralNeighborStyleTransfer