  This paper presents an automatic image synthesis method to transfer the style
of an example image to a content image. When standard neural style transfer
approaches are used, the textures and colours in different semantic regions of
the style image are often applied inappropriately to the content image,
ignoring its semantic layout, and ruining the transfer result. In order to
reduce or avoid such effects, we propose a novel method based on automatically
segmenting the objects and extracting their soft semantic masks from the style
and content images, in order to preserve the structure of the content image
while having the style transferred. Each soft mask of the style image
represents a specific part of the style image, corresponding to the soft mask
of the content image with the same semantics. Both the soft masks and source
images are provided as multichannel input to an augmented deep CNN framework
for style transfer which incorporates a generative Markov random field (MRF)
model. Results on various images show that our method outperforms the most
recent techniques.
