Recent studies have shown remarkable success in universal style transfer which transfers arbitrary visual styles to content images. However, existing approaches suffer from the aesthetic-unrealistic problem that introduces disharmonious patterns and evident artifacts, making the results easy to spot from real paintings. To address this limitation, we propose AesUST, a novel Aesthetic-enhanced Universal Style Transfer approach that can generate aesthetically more realistic and pleasing results for arbitrary styles. Specifically, our approach introduces an aesthetic discriminator to learn the universal human-delightful aesthetic features from a large corpus of artist-created paintings. Then, the aesthetic features are incorporated to enhance the style transfer process via a novel Aesthetic-aware Style-Attention (AesSA) module. Such an AesSA module enables our AesUST to efficiently and flexibly integrate the style patterns according to the global aesthetic channel distribution of the style image and the local semantic spatial distribution of the content image. Moreover, we also develop a new two-stage transfer training strategy with two aesthetic regularizations to train our model more effectively, further improving stylization performance. Extensive experiments and user studies demonstrate that our approach synthesizes aesthetically more harmonious and realistic results than state of the art, greatly narrowing the disparity with real artist-created paintings. Our code is available at https://github.com/EndyWon/AesUST.

本研究提出了一种新的美学增强的通用风格转移方法，通过引入美学鉴别器和美学感知的注意力机制，以及两阶段的训练策略和正则化方法，实现了对任意风格的艺术作品进行更加真实和美学的风格转移。

AesUST: 朝着美学增强的通用风格转移