@inproceedings{
hong2024consplan,
title={Cons2Plan: Vector Floorplan Generation from Various Conditions via a  Learning Framework based on Conditional Diffusion Models},
author={Shibo Hong and Xuhong Zhang and Tianyu Du and Sheng Cheng and Xun Wang and Jianwei Yin},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=xnpEpIqjUU}
}
The field of floorplan generation has attracted significant interest from the community. Remarkably, recent progress in methods based on generative models has substantially promoted the development of floorplan generation. However, generating floorplans that satisfy various conditions remains a challenging task. This paper proposes a learning framework, named Cons2Plan, for automatically and high-quality generating vector floorplans from various conditions. The input conditions can be graphs, boundaries, or a combination of both. The conditional diffusion model is the core component of our Cons2Plan. The denoising network uses a conditional embedding module to incorporate the conditions as guidance during the reverse process. Additionally, Cons2Plan incorporates a two-stage approach that generates graph conditions based on boundaries. It utilizes three regression models for node prediction and a novel conditional edge generation diffusion model, named CEDM, for edge generation. We conduct qualitative evaluations, quantitative comparisons, and ablation studies to demonstrate that our method can produce higher-quality floorplans than those generated by state-of-the-art methods.
Cons2Plan: Vector Floorplan Generation from Various Conditions via a  Learning Framework based on Conditional Diffusion Models
Shibo Hong Xuhong Zhang Tianyu Du Sheng Cheng Xun Wang Jianwei Yin