  This paper addresses the challenge of object-centric layout generation under
spatial constraints, seen in multiple domains including floorplan design
process. The design process typically involves specifying a set of spatial
constraints that include object attributes like size and inter-object relations
such as relative positioning. Existing works, which typically represent objects
as single nodes, lack the granularity to accurately model complex interactions
between objects. For instance, often only certain parts of an object, like a
room's right wall, interact with adjacent objects. To address this gap, we
introduce a factor graph based approach with four latent variable nodes for
each room, and a factor node for each constraint. The factor nodes represent
dependencies among the variables to which they are connected, effectively
capturing constraints that are potentially of a higher order. We then develop
message-passing on the bipartite graph, forming a factor graph neural network
that is trained to produce a floorplan that aligns with the desired
requirements. Our approach is simple and generates layouts faithful to the user
requirements, demonstrated by a large improvement in IOU scores over existing
methods. Additionally, our approach, being inferential and accurate, is
well-suited to the practical human-in-the-loop design process where
specifications evolve iteratively, offering a practical and powerful tool for
AI-guided design.
