  Arbitrary style transfer has been demonstrated to be efficient in artistic
image generation. Previous methods either globally modulate the content feature
ignoring local details, or overly focus on the local structure details leading
to style leakage. In contrast to the literature, we propose a new scheme
\textit{``style kernel"} that learns {\em spatially adaptive kernels} for
per-pixel stylization, where the convolutional kernels are dynamically
generated from the global style-content aligned feature and then the learned
kernels are applied to modulate the content feature at each spatial position.
This new scheme allows flexible both global and local interactions between the
content and style features such that the wanted styles can be easily
transferred to the content image while at the same time the content structure
can be easily preserved. To further enhance the flexibility of our style
transfer method, we propose a Style Alignment Encoding (SAE) module
complemented with a Content-based Gating Modulation (CGM) module for learning
the dynamic style kernels in focusing regions. Extensive experiments strongly
demonstrate that our proposed method outperforms state-of-the-art methods and
exhibits superior performance in terms of visual quality and efficiency.
