  Arbitrary image style transfer is a challenging task which aims to stylize a
content image conditioned on arbitrary style images. In this task the
feature-level content-style transformation plays a vital role for proper fusion
of features. Existing feature transformation algorithms often suffer from loss
of content or style details, non-natural stroke patterns, and unstable
training. To mitigate these issues, this paper proposes a new feature-level
style transformation technique, named Style Projection, for parameter-free,
fast, and effective content-style transformation. This paper further presents a
real-time feed-forward model to leverage Style Projection for arbitrary image
style transfer, which includes a regularization term for matching the semantics
between input contents and stylized outputs. Extensive qualitative analysis,
quantitative evaluation, and user study have demonstrated the effectiveness and
efficiency of the proposed methods.
