Artistic style transfer aims to create new artistic images by rendering a given photograph with the target artistic style. Existing methods learn styles simply based on global statistics or local patches, lacking careful consideration of the drawing process in practice. Consequently, the stylization results either fail to capture abundant and diversified local style patterns, or contain undesired semantic information of the style image and deviate from the global style distribution. To address this issue, we imitate the drawing process of humans and propose a Two-Stage Statistics-Aware Transformation (TSSAT) module, which first builds the global style foundation by aligning the global statistics of content and style features and then further enriches local style details by swapping the local statistics (instead of local features) in a patch-wise manner, significantly improving the stylization effects. Moreover, to further enhance both content and style representations, we introduce two novel losses: an attention-based content loss and a patch-based style loss, where the former enables better content preservation by enforcing the semantic relation in the content image to be retained during stylization, and the latter focuses on increasing the local style similarity between the style and stylized images. Extensive qualitative and quantitative experiments verify the effectiveness of our method.

翻译：艺术风格迁移旨在通过将给定照片渲染为目标艺术风格来创作新的艺术图像。现有方法仅基于全局统计量或局部补丁学习风格，缺乏对实际绘画过程的细致考量。因此，风格化结果要么无法捕获丰富多样的局部风格模式，要么包含风格图像的无关语义信息并偏离全局风格分布。为解决此问题，我们模仿人类绘画过程，提出两阶段统计感知变换（TSSAT）模块：首先通过对齐内容特征与风格特征的全局统计量建立全局风格基础，再通过以补丁方式交换局部统计量（而非局部特征）进一步丰富局部风格细节，显著提升了风格化效果。此外，为增强内容和风格表征，我们引入两种新型损失函数：基于注意力的内容损失和基于补丁的风格损失。前者通过在风格化过程中强制保留内容图像的语义关系实现更好的内容保持，后者则专注于提升风格图像与风格化图像之间的局部风格相似性。大量定性与定量实验验证了本方法的有效性。