  Autonomous agents, such as driverless cars, require large amounts of labeled
visual data for their training. A viable approach for acquiring such data is
training a generative model with collected real data, and then augmenting the
collected real dataset with synthetic images from the model, generated with
control of the scene layout and ground truth labeling. In this paper we propose
Full-Glow, a fully conditional Glow-based architecture for generating plausible
and realistic images of novel street scenes given a semantic segmentation map
indicating the scene layout. Benchmark comparisons show our model to outperform
recent works in terms of the semantic segmentation performance of a pretrained
PSPNet. This indicates that images from our model are, to a higher degree than
from other models, similar to real images of the same kinds of scenes and
objects, making them suitable as training data for a visual semantic
segmentation or object recognition system.
