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.

本研究提出了新的、广义的标准化模块——Dynamic Instance Normalization（DIN），通过组成实例标准化和动态卷积将风格图像编码成可学习的卷积参数，从而实现了灵活且更高效的任意风格转移。实验结果表明，所提出的方法在具有挑战性的风格模式上具有非常鼓舞人心的结果，并且是使用基于MobileNet的轻量级架构进行任意风格转移的首次，并且在计算成本方面比现有方法提供了超过20倍的降低因素。此外，所提出的DIN为最先进的卷积操作提供了灵活的支持，因此触发了新的功能，如非自然图像的均匀笔触位置和自动空间笔触控制。

任意风格迁移的动态实例规范化