Abstract When standard neural style transfer approaches are used in portrait style transfer, they often inappropriately apply textures and colours in different regions of the style portraits to the content portraits, leading to unsatisfied transfer results. This paper presents a portrait style transfer method to transfer the style of one image to another. It first proposes a combined segmentation method for the portrait parts, which segments both the style portrait and the content portrait into masks of seven parts automatically, including background, face, eyes, nose, eyebrows, mouth and foreground. These masks are extracted to capture elements of the styles for objects in the style image and to preserve the structure in the content portrait. This paper then proposes an augmented deep Convolutional Neural Network (CNN) framework for portrait style transfer. The masks of seven parts are added into a trained deep convolutional neural network as feature maps in certain selected layers in the augmented deep CNN model. An improved loss function is proposed for the training of the portrait style transfer. Results on various images show that our method outperforms the state-of-the-art style transfer techniques.

中文翻译：

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使用深度卷积神经网络和面部分割的人像风格迁移

摘要 在肖像风格迁移中使用标准的神经风格迁移方法时，往往将风格肖像不同区域的纹理和颜色不适当地应用于内容肖像，导致迁移结果不令人满意。本文提出了一种肖像风格迁移方法，将一幅图像的风格迁移到另一幅图像。首先提出了人像部分的组合分割方法，将风格人像和内容人像自动分割为背景、人脸、眼睛、鼻子、眉毛、嘴巴和前景七个部分的蒙版。提取这些掩码以捕获样式图像中对象的样式元素并保留内容肖像中的结构。然后，本文提出了一种用于肖像风格迁移的增强型深度卷积神经网络 (CNN) 框架。七个部分的掩码被添加到经过训练的深度卷积神经网络中，作为增强深度 CNN 模型中某些选定层中的特征图。提出了一种改进的损失函数用于人像风格迁移的训练。各种图像的结果表明，我们的方法优于最先进的风格转移技术。