  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.
