论文标题
FACE Sketch Shetch合成使用金字塔列的样式传输功能
Face Sketch Synthesis with Style Transfer using Pyramid Column Feature
论文作者
论文摘要
在本文中，我们提出了一个基于深层神经网络的新型框架，以从照片中进行素描综合。模仿艺术家如何绘制草图的过程，我们的框架以级联的方式综合了面部素描。首先生成内容图像，概述了面部形状和关键面部特征。然后添加纹理和阴影以丰富草图的详细信息。我们利用完全卷积的神经网络（FCNN）来创建内容图像，并提出一种样式转移方法，以基于新提出的金字塔列功能引入纹理和阴影。我们证明，基于金字塔列功能的样式转移方法不仅可以保留比共同样式转移方法更多的草图细节，而且还可以超过传统的基于补丁的方法。定量和定性评估表明，我们的框架优于其他最先进的方法，也可以很好地推广到不同的测试图像。代码可在https://github.com/chaofengc/face-sketch上找到
In this paper, we propose a novel framework based on deep neural networks for face sketch synthesis from a photo. Imitating the process of how artists draw sketches, our framework synthesizes face sketches in a cascaded manner. A content image is first generated that outlines the shape of the face and the key facial features. Textures and shadings are then added to enrich the details of the sketch. We utilize a fully convolutional neural network (FCNN) to create the content image, and propose a style transfer approach to introduce textures and shadings based on a newly proposed pyramid column feature. We demonstrate that our style transfer approach based on the pyramid column feature can not only preserve more sketch details than the common style transfer method, but also surpasses traditional patch based methods. Quantitative and qualitative evaluations suggest that our framework outperforms other state-of-the-arts methods, and can also generalize well to different test images. Codes are available at https://github.com/chaofengc/Face-Sketch
