Artistic Style Discovery with Independent Components

Xin Xie, Yi Li, Huaibo Huang, Haiyan Fu, Wanwan Wang, Yanqing Guo

Abstract:

Style transfer has been well studied in recent years with
excellent performance processed. While existing methods
usually choose CNNs as the powerful tool to accomplish
superb stylization, less attention was paid to the latent style
space. Rare exploration of underlying dimensions results in
the poor style controllability and the limited practical application. In this work, we rethink the internal meaning of
style features, further proposing a novel unsupervised algorithm for style discovery and achieving personalized manipulation. In particular, we take a closer look into the mechanism of style transfer and obtain different artistic style components from the latent space consisting of different style
features. Then fresh styles can be generated by linear combination according to various style components. Experimental results have shown that our approach is superb in 1)
restylizing the original output with the diverse artistic styles
discovered from the latent space while keeping the content
unchanged, and 2) being generic and compatible for various style transfer methods. Our code is available in this
page: https://github.com/Shelsin/ArtIns.