Recent style transfer models have provided promising artistic results.
However, given a photograph as a reference style, existing methods are limited
by spatial distortions or unrealistic artifacts, which should not happen in
real photographs. We introduce a theoretically sound correction to the network
architecture that remarkably enhances photorealism and faithfully transfers the
style. The key ingredient of our method is wavelet transforms that naturally
fits in deep networks. We propose a wavelet corrected transfer based on
whitening and coloring transforms (WCT$^2$) that allows features to preserve
their structural information and statistical properties of VGG feature space
during stylization. This is the first and the only end-to-end model that can
stylize a $1024\times1024$ resolution image in 4.7 seconds, giving a pleasing
and photorealistic quality without any post-processing. Last but not least, our
model provides a stable video stylization without temporal constraints. Our
code, generated images, and pre-trained models are all available at
this https URL

本篇文章提出了一种基于小波变换和白化和彩色变换（WCT$^2$）的样式转换方法，该方法引入了一种理论上正确的修正网络架构，其可能性大大增强了感性质地和两种样式之间的转化，同时保持了结构信息和 VGG 特征空间的统计属性；此外，被提出的模型可以在 4.7 秒内完成 $1024	imes1024$ 分辨率图像的样式转换，且无需后期处理，具有稳定的视频样式转换。