In recent years, image style transfer has been greatly improved by using deep learning technology. However, when directly applied to clothing style transfer, the current methods cannot allow the users to self-control the local transfer position of an image, such as separating specific T-shirt or trousers from a figure, and cannot achieve the perfect preservation of clothing shape. Therefore, this paper proposes an interactive image localized style transfer method especially for clothes. We introduce additional image called outline image, which is extracted from content image by interactive algorithm. The interaction consists simply of dragging a rectangle around the desired clothing. Then, we introduce an outline loss function based on distance transform of the outline image, which can achieve the perfect preservation of clothing shape. In order to smooth and denoise the boundary region, total variation regularization is employed. The proposed method constrains that the new style is generated only in the desired clothing part rather than the whole image including background. Therefore, in our new generated images, the original clothing shape can be reserved perfectly. Experiment results show impressive generated clothing images and demonstrate that this is a good approach to design clothes.

中文翻译：

---

基于CNN和交互式分割的图像局部风格转移到设计服装中。

近年来，使用深度学习技术极大地改善了图像样式转换。但是，当直接应用于服装风格转移时，当前的方法不能允许用户自控图像的局部转移位置，例如将特定的T恤或裤子与人物分开，无法完美地保存服装。形状。因此，本文提出了一种针对服装的交互式图像局部风格转移方法。我们引入了称为轮廓图的附加图像，它是通过交互式算法从内容图像中提取的。交互仅包括在所需衣服周围拖动一个矩形。然后，我们基于轮廓图像的距离变换引入轮廓损失函数，可以实现服装形状的完美保存。为了使边界区域平滑和去噪，采用了总变化正则化。所提出的方法限制了仅在期望的衣物部分而不是包括背景的整个图像中产生新样式。因此，在我们新生成的图像中，可以完美保留原始的衣服形状。实验结果显示出令人印象深刻的服装图像，并证明这是设计服装的好方法。