To deliver the artistic expression of the target style, recent studies exploit the attention mechanism owing to its ability to map the local patches of the style image to the corresponding patches of the content image. However, because of the low semantic correspondence between arbitrary content and artworks, the attention module repeatedly abuses specific local patches from the style image, resulting in disharmonious and evident repetitive artifacts. To overcome this limitation and accomplish impeccable artistic style transfer, we focus on enhancing the attention mechanism and capturing the rhythm of patterns that organize the style. In this paper, we introduce a novel metric, namely pattern repeatability, that quantifies the repetition of patterns in the style image. Based on the pattern repeatability, we propose Aesthetic Pattern-Aware style transfer Networks (AesPA-Net) that discover the sweet spot of local and global style expressions. In addition, we propose a novel self-supervisory task to encourage the attention mechanism to learn precise and meaningful semantic correspondence. Lastly, we introduce the patch-wise style loss to transfer the elaborate rhythm of local patterns. Through qualitative and quantitative evaluations, we verify the reliability of the proposed pattern repeatability that aligns with human perception, and demonstrate the superiority of the proposed framework.

翻译：为实现目标风格的艺术表达，近期研究利用注意力机制将风格图像的局部区域映射至内容图像的对应区域。然而，由于任意内容与艺术作品间语义对应性较弱，注意力模块会重复滥用风格图像中的特定局部区域，导致产生不协调且明显的重复伪影。为克服这一局限并实现完美的艺术风格迁移，我们聚焦于增强注意力机制并捕捉构成风格模式的韵律。本文提出一种新型度量指标——模式可重复性，用于量化风格图像中模式的重复程度。基于该指标，我们提出美学模式感知风格迁移网络（AesPA-Net），旨在发掘局部与全局风格表达的平衡点。此外，我们提出一种新颖的自监督任务，以促使注意力机制学习精确且有意义的语义对应关系。最后，引入分块风格损失函数以传递局部模式的精细韵律。通过定性与定量评估，我们验证了所提模式可重复性与人类感知一致性的可靠性，并证明了所提框架的优越性。