  Generation of stroke-based non-photorealistic imagery, is an important
problem in the computer vision community. As an endeavor in this direction,
substantial recent research efforts have been focused on teaching machines "how
to paint", in a manner similar to a human painter. However, the applicability
of previous methods has been limited to datasets with little variation in
position, scale and saliency of the foreground object. As a consequence, we
find that these methods struggle to cover the granularity and diversity
possessed by real world images. To this end, we propose a Semantic Guidance
pipeline with 1) a bi-level painting procedure for learning the distinction
between foreground and background brush strokes at training time. 2) We also
introduce invariance to the position and scale of the foreground object through
a neural alignment model, which combines object localization and spatial
transformer networks in an end to end manner, to zoom into a particular
semantic instance. 3) The distinguishing features of the in-focus object are
then amplified by maximizing a novel guided backpropagation based focus reward.
The proposed agent does not require any supervision on human stroke-data and
successfully handles variations in foreground object attributes, thus,
producing much higher quality canvases for the CUB-200 Birds and Stanford
Cars-196 datasets. Finally, we demonstrate the further efficacy of our method
on complex datasets with multiple foreground object instances by evaluating an
extension of our method on the challenging Virtual-KITTI dataset. Source code
and models are available at https://github.com/1jsingh/semantic-guidance.
