HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising

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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.