Abstract
Artistic style transfer has long been possible with the advancements of convolution- and transformer-based neural networks. Most algorithms apply the artistic style transfer to the whole image, but individual users may only need to apply a style transfer to a specific region in the image. The standard practice is to simply mask the image after the stylization. This work shows that this approach tends to improperly capture the style features in the region of interest. We propose a partial-convolution-based style transfer network that accurately applies the style features exclusively to the region of interest. Additionally, we present network-internal blending techniques that account for imperfections in the region selection. We show that this visually and quantitatively improves stylization using examples from the SA-1B dataset. Code is publicly available at this https URL.
Abstract (translated)
基于卷积和转换器神经网络的发展，艺术风格转移已经可以实现很长时间了。大多数算法将艺术风格转移应用于整个图像，但个别用户可能只需要对图像中的特定区域进行风格化处理。标准做法是在完成样式化后简单地用蒙版遮盖不需要的区域。这项研究表明，这种方法往往无法适当地捕捉到感兴趣区域内的风格特征。为此，我们提出了一种基于部分卷积的艺术风格转移网络，能够精确地将风格特征应用到感兴趣的特定区域内。此外，我们还介绍了网络内部融合技术来处理区域选择中的不完美之处。通过使用SA-1B数据集的例子，我们展示了这种做法在视觉和定量上都提高了样式化的质量。代码已公开发布在[提供的链接]。
URL
https://arxiv.org/abs/2508.05769
