  Style transfer is the process of rendering one image with some content in the
style of another image, representing the style. Recent studies of Liu et al.
(2017) show that traditional style transfer methods of Gatys et al. (2016) and
Johnson et al. (2016) fail to reproduce the depth of the content image, which
is critical for human perception. They suggest to preserve the depth map by
additional regularizer in the optimized loss function, forcing preservation of
the depth map. However these traditional methods are either computationally
inefficient or require training a separate neural network for each style. AdaIN
method of Huang et al. (2017) allows efficient transferring of arbitrary style
without training a separate model but is not able to reproduce the depth map of
the content image. We propose an extension to this method, allowing depth map
preservation by applying variable stylization strength. Qualitative analysis
and results of user evaluation study indicate that the proposed method provides
better stylizations, compared to the original AdaIN style transfer method.
