  Style transfer has been an important topic both in computer vision and
graphics. Since the seminal work of Gatys et al. first demonstrates the power
of stylization through optimization in the deep feature space, quite a few
approaches have achieved real-time arbitrary style transfer with
straightforward statistic matching techniques. In this work, our key
observation is that only considering features in the input style image for the
global deep feature statistic matching or local patch swap may not always
ensure a satisfactory style transfer; see e.g., Figure 1. Instead, we propose a
novel transfer framework, EFANet, that aims to jointly analyze and better align
exchangeable features extracted from content and style image pair. In this way,
the style features from the style image seek for the best compatibility with
the content information in the content image, leading to more structured
stylization results. In addition, a new whitening loss is developed for
purifying the computed content features and better fusion with styles in
feature space. Qualitative and quantitative experiments demonstrate the
advantages of our approach.
