论文标题
使用实例归一化的深度感知神经样式转移
Depth-aware Neural Style Transfer using Instance Normalization
论文作者
论文摘要
神经风格转移（NST）与视觉媒体的艺术风格有关。它可以描述为将艺术形象风格转移到普通照片上的过程。最近，许多研究考虑了NST算法的深度保护功能的增强，以解决输入内容图像在各个深度中包含许多对象时发生的不希望的效果。我们的方法使用深度剩余的卷积网络与实例归一化层，该层利用高级深度预测网络将深度保存整合为内容和样式的附加损失函数。我们展示了有效保留内容图像的深度和全局结构的结果。三个不同的评估过程表明，我们的系统能够保留风格化结果的结构，同时表现出样式捕捉功能和美学质量，或与最先进的方法相当或优越。项目页面：https：//ioannoue.github.io/depth-aware-nst-sing-in.html。
Neural Style Transfer (NST) is concerned with the artistic stylization of visual media. It can be described as the process of transferring the style of an artistic image onto an ordinary photograph. Recently, a number of studies have considered the enhancement of the depth-preserving capabilities of the NST algorithms to address the undesired effects that occur when the input content images include numerous objects at various depths. Our approach uses a deep residual convolutional network with instance normalization layers that utilizes an advanced depth prediction network to integrate depth preservation as an additional loss function to content and style. We demonstrate results that are effective in retaining the depth and global structure of content images. Three different evaluation processes show that our system is capable of preserving the structure of the stylized results while exhibiting style-capture capabilities and aesthetic qualities comparable or superior to state-of-the-art methods. Project page: https://ioannoue.github.io/depth-aware-nst-using-in.html.
