  Prior normalization methods rely on affine transformations to produce
arbitrary image style transfers, of which the parameters are computed in a
pre-defined way. Such manually-defined nature eventually results in the
high-cost and shared encoders for both style and content encoding, making style
transfer systems cumbersome to be deployed in resource-constrained environments
like on the mobile-terminal side. In this paper, we propose a new and
generalized normalization module, termed as Dynamic Instance Normalization
(DIN), that allows for flexible and more efficient arbitrary style transfers.
Comprising an instance normalization and a dynamic convolution, DIN encodes a
style image into learnable convolution parameters, upon which the content image
is stylized. Unlike conventional methods that use shared complex encoders to
encode content and style, the proposed DIN introduces a sophisticated style
encoder, yet comes with a compact and lightweight content encoder for fast
inference. Experimental results demonstrate that the proposed approach yields
very encouraging results on challenging style patterns and, to our best
knowledge, for the first time enables an arbitrary style transfer using
MobileNet-based lightweight architecture, leading to a reduction factor of more
than twenty in computational cost as compared to existing approaches.
Furthermore, the proposed DIN provides flexible support for state-of-the-art
convolutional operations, and thus triggers novel functionalities, such as
uniform-stroke placement for non-natural images and automatic spatial-stroke
control.
