  Fast arbitrary neural style transfer has attracted widespread attention from
academic, industrial and art communities due to its flexibility in enabling
various applications. Existing solutions either attentively fuse deep style
feature into deep content feature without considering feature distributions, or
adaptively normalize deep content feature according to the style such that
their global statistics are matched. Although effective, leaving shallow
feature unexplored and without locally considering feature statistics, they are
prone to unnatural output with unpleasing local distortions. To alleviate this
problem, in this paper, we propose a novel attention and normalization module,
named Adaptive Attention Normalization (AdaAttN), to adaptively perform
attentive normalization on per-point basis. Specifically, spatial attention
score is learnt from both shallow and deep features of content and style
images. Then per-point weighted statistics are calculated by regarding a style
feature point as a distribution of attention-weighted output of all style
feature points. Finally, the content feature is normalized so that they
demonstrate the same local feature statistics as the calculated per-point
weighted style feature statistics. Besides, a novel local feature loss is
derived based on AdaAttN to enhance local visual quality. We also extend
AdaAttN to be ready for video style transfer with slight modifications.
Experiments demonstrate that our method achieves state-of-the-art arbitrary
image/video style transfer. Codes and models are available.
