  Despite recent advancements in single-domain or single-object image
generation, it is still challenging to generate complex scenes containing
diverse, multiple objects and their interactions. Scene graphs, composed of
nodes as objects and directed-edges as relationships among objects, offer an
alternative representation of a scene that is more semantically grounded than
images. We hypothesize that a generative model for scene graphs might be able
to learn the underlying semantic structure of real-world scenes more
effectively than images, and hence, generate realistic novel scenes in the form
of scene graphs. In this work, we explore a new task for the unconditional
generation of semantic scene graphs. We develop a deep auto-regressive model
called SceneGraphGen which can directly learn the probability distribution over
labelled and directed graphs using a hierarchical recurrent architecture. The
model takes a seed object as input and generates a scene graph in a sequence of
steps, each step generating an object node, followed by a sequence of
relationship edges connecting to the previous nodes. We show that the scene
graphs generated by SceneGraphGen are diverse and follow the semantic patterns
of real-world scenes. Additionally, we demonstrate the application of the
generated graphs in image synthesis, anomaly detection and scene graph
completion.
