Content affinity loss including feature and pixel affinity is a main problem which leads to artifacts in photorealistic and video style transfer. This paper proposes a new framework named CAP-VSTNet, which consists of a new reversible residual network and an unbiased linear transform module, for versatile style transfer. This reversible residual network can not only preserve content affinity but not introduce redundant information as traditional reversible networks, and hence facilitate better stylization. Empowered by Matting Laplacian training loss which can address the pixel affinity loss problem led by the linear transform, the proposed framework is applicable and effective on versatile style transfer. Extensive experiments show that CAP-VSTNet can produce better qualitative and quantitative results in comparison with the state-of-the-art methods.

翻译：内容亲和性损失包括特征和像素亲和力是照片逼真和视频风格转移中导致伪影的主要问题。本文提出了一种新的框架，名为CAP-VSTNet，它由一个新的可逆残差网络和一个无偏线性变换模块组成，用于多功能风格转移。这个可逆残差网络不仅可以保持内容亲和力，而且不像传统的可逆网络那样引入冗余信息，从而促进更好的样式化。由于使用了可以解决线性变换导致的像素亲和丢失问题的Matting Laplacian训练损失，因此提出的框架可以适用于多功能风格转移，并且非常有效。广泛的实验表明，CAP-VSTNet可以产生比现有方法更好的定性和定量结果。