  Convolutional neural networks (CNNs) have proven highly effective at image
synthesis and style transfer. For most users, however, using them as tools can
be a challenging task due to their unpredictable behavior that goes against
common intuitions. This paper introduces a novel concept to augment such
generative architectures with semantic annotations, either by manually
authoring pixel labels or using existing solutions for semantic segmentation.
The result is a content-aware generative algorithm that offers meaningful
control over the outcome. Thus, we increase the quality of images generated by
avoiding common glitches, make the results look significantly more plausible,
and extend the functional range of these algorithms---whether for portraits or
landscapes, etc. Applications include semantic style transfer and turning
doodles with few colors into masterful paintings!
