Style transfer has been an important topic in both computer vision and graphics. Gatys et al. first prove that deep features extracted by the pre-trained VGG network represent both content and style features of an image and hence, style transfer can be achieved through optimization in feature space. Huang et al. then show that real-time arbitrary style transfer can be done by simply aligning the mean and variance of each feature channel. In this paper, however, we argue that only aligning the global statistics of deep features cannot always guarantee a good style transfer. Instead, we propose to jointly analyze the input image pair and extract common/exchangeable style features between the two. Besides, a new fusion mode is developed for combining content and style information in feature space. Qualitative and quantitative experiments demonstrate the advantages of our approach.

本研究提出了一个称为EFANet的新型转换框架，它考虑交换特征并分析内容和样式图像对提取的特征进行更好的对齐，以实现更好的结构化风格化结果，并开发了一种新的白化损失来净化计算出的内容特征并更好地融合特征空间中的样式。定量和定性实验都证明了我们方法的优越性。

EFANet: 可交换特征对齐网路用于任意风格转换