  Synthesizing realistic and diverse indoor 3D scene layouts in a controllable
fashion opens up applications in simulated navigation and virtual reality. As
concise and robust representations of a scene, scene graphs have proven to be
well-suited as the semantic control on the generated layout. We present a
variant of the conditional variational autoencoder (cVAE) model to synthesize
3D scenes from scene graphs and floor plans. We exploit the properties of
self-attention layers to capture high-level relationships between objects in a
scene, and use these as the building blocks of our model. Our model, leverages
graph transformers to estimate the size, dimension and orientation of the
objects in a room while satisfying relationships in the given scene graph. Our
experiments shows self-attention layers leads to sparser (7.9x compared to
Graphto3D) and more diverse scenes (16%).
