  Automatic image synthesis research has been rapidly growing with deep
networks getting more and more expressive. In the last couple of years, we have
observed images of digits, indoor scenes, birds, chairs, etc. being
automatically generated. The expressive power of image generators have also
been enhanced by introducing several forms of conditioning variables such as
object names, sentences, bounding box and key-point locations. In this work, we
propose a novel deep conditional generative adversarial network architecture
that takes its strength from the semantic layout and scene attributes
integrated as conditioning variables. We show that our architecture is able to
generate realistic outdoor scene images under different conditions, e.g.
day-night, sunny-foggy, with clear object boundaries.
