  We present a structured graph variational autoencoder for generating the
layout of indoor 3D scenes. Given the room type (e.g., living room or library)
and the room layout (e.g., room elements such as floor and walls), our
architecture generates a collection of objects (e.g., furniture items such as
sofa, table and chairs) that is consistent with the room type and layout. This
is a challenging problem because the generated scene should satisfy multiple
constrains, e.g., each object must lie inside the room and two objects cannot
occupy the same volume. To address these challenges, we propose a deep
generative model that encodes these relationships as soft constraints on an
attributed graph (e.g., the nodes capture attributes of room and furniture
elements, such as class, pose and size, and the edges capture geometric
relationships such as relative orientation). The architecture consists of a
graph encoder that maps the input graph to a structured latent space, and a
graph decoder that generates a furniture graph, given a latent code and the
room graph. The latent space is modeled with auto-regressive priors, which
facilitates the generation of highly structured scenes. We also propose an
efficient training procedure that combines matching and constrained learning.
Experiments on the 3D-FRONT dataset show that our method produces scenes that
are diverse and are adapted to the room layout.
