  Universal Neural Style Transfer (NST) methods are capable of performing style
transfer of arbitrary styles in a style-agnostic manner via feature transforms
in (almost) real-time. Even though their unimodal parametric style modeling
approach has been proven adequate to transfer a single style from relatively
simple images, they are usually not capable of effectively handling more
complex styles, producing significant artifacts, as well as reducing the
quality of the synthesized textures in the stylized image. To overcome these
limitations, in this paper we propose a novel universal NST approach that
separately models each sub-style that exists in a given style image (or a
collection of style images). This allows for better modeling the subtle style
differences within the same style image and then using the most appropriate
sub-style (or mixtures of different sub-styles) to stylize the content image.
The ability of the proposed approach to a) perform a wide range of different
stylizations using the sub-styles that exist in one style image, while giving
the ability to the user to appropriate mix the different sub-styles, b)
automatically match the most appropriate sub-style to different semantic
regions of the content image, improving existing state-of-the-art universal NST
approaches, and c) detecting and transferring the sub-styles from collections
of images are demonstrated through extensive experiments.
