  The applicability of computer vision to real paintings and artworks has been
rarely investigated, even though a vast heritage would greatly benefit from
techniques which can understand and process data from the artistic domain. This
is partially due to the small amount of annotated artistic data, which is not
even comparable to that of natural images captured by cameras. In this paper,
we propose a semantic-aware architecture which can translate artworks to
photo-realistic visualizations, thus reducing the gap between visual features
of artistic and realistic data. Our architecture can generate natural images by
retrieving and learning details from real photos through a similarity matching
strategy which leverages a weakly-supervised semantic understanding of the
scene. Experimental results show that the proposed technique leads to increased
realism and to a reduction in domain shift, which improves the performance of
pre-trained architectures for classification, detection, and segmentation. Code
is publicly available at: https://github.com/aimagelab/art2real.
