Abstract
Artistic style transfer aims to use a style image and a content image to synthesize a target image that retains the same artistic expression as the style image while preserving the basic content of the content image. Many recently proposed style transfer methods have a common problem; that is, they simply transfer the texture and color of the style image to the global structure of the content image. As a result, the content image has a local structure that is not similar to the local structure of the style image. In this paper, we present an effective method that can be used to transfer style patterns while fusing the local style structure into the local content structure. In our method, dif-ferent levels of coarse stylized features are first reconstructed at low resolution using a Coarse Network, in which style color distribution is roughly transferred, and the content structure is combined with the style structure. Then, the reconstructed features and the content features are adopted to synthesize high-quality structure-aware stylized images with high resolution using a Fine Network with three structural selective fusion (SSF) modules. The effectiveness of our method is demonstrated through the generation of appealing high-quality stylization results and a com-parison with some state-of-the-art style transfer methods.
Abstract (translated)
艺术风格转换的目标是利用一幅风格图像和一幅内容图像来合成目标图像，使其保留与风格图像相同的艺术表达的同时保持内容图像的基本内容不变。然而，许多最近提出的风格转移方法存在一个共同的问题：它们只是简单地将风格图像的纹理和颜色转移到内容图像的整体结构上。因此，生成的内容图像在局部结构上往往并不类似风格图像的局部结构。 在这篇论文中，我们提出了一种有效的方法，可以用于在融合局部风格结构到局部内容结构的同时进行样式图案转移。我们的方法首先使用一个粗略网络（Coarse Network）在低分辨率下重构不同级别的粗略风格化特征，在此过程中大致传输了风格颜色分布，并结合了内容与风格的结构。然后，通过一个配备有三个结构选择性融合（SSF）模块的精细网络（Fine Network），利用重建后的特征和内容特征来生成高分辨率且具有高度感知结构意识的风格化图像。 我们的方法的有效性通过生成吸引人的高质量风格化结果以及与其他最新风格转移方法进行比较得到了证明。
URL
https://arxiv.org/abs/2502.05387
