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

• URL: http://arxiv.org/abs/2211.13287v1
• Date: Wed, 23 Nov 2022 20:25:11 GMT
• Title: HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising
• Authors: Mohammad Amin Shabani, Sepidehsadat Hosseini, Yasutaka Furukawa
• Abstract summary: The paper presents a novel approach for vector-floorplan generation via a diffusion model. We represent a floorplan as 1D polygonal loops, each of which corresponds to a room or a door. The proposed approach makes significant improvements in all the metrics against the state-of-the-art with significant margins.
• Score: 26.2029195029127
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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