  The paper presents a novel approach for vector-floorplan generation via a
diffusion model, which denoises 2D coordinates of room/door corners with two
inference objectives: 1) a single-step noise as the continuous quantity to
precisely invert the continuous forward process; and 2) the final 2D coordinate
as the discrete quantity to establish geometric incident relationships such as
parallelism, orthogonality, and corner-sharing. Our task is graph-conditioned
floorplan generation, a common workflow in floorplan design. We represent a
floorplan as 1D polygonal loops, each of which corresponds to a room or a door.
Our diffusion model employs a Transformer architecture at the core, which
controls the attention masks based on the input graph-constraint and directly
generates vector-graphics floorplans via a discrete and continuous denoising
process. We have evaluated our approach on RPLAN dataset. The proposed approach
makes significant improvements in all the metrics against the state-of-the-art
with significant margins, while being capable of generating non-Manhattan
structures and controlling the exact number of corners per room. A project
website with supplementary video and document is here
https://aminshabani.github.io/housediffusion.
