End-to-end Generative Floor-plan and Layout with Attributes and Relation Graph
This work aims to accelerate the production of interior decoration solutions for professional interior designers by automating furniture layout generation.
This paper introduces an end-to-end model that generates furniture layouts for interior scenes from a random vector. The model combines three modules to enhance auto-layout generation given room dimensional categories, producing higher-quality layouts compared to state-of-the-art models on a dataset of 191,208 professional designs.
In this paper, we propose an end-end model for producing furniture layout for interior scene synthesis from the random vector. This proposed model is aimed to support professional interior designers to produce the interior decoration solutions more quickly. The proposed model combines a conditional floor-plan module of the room, a conditional graphical floor-plan module of the room and a conditional layout module. As compared with the prior work on scene synthesis, our proposed three modules enhance the ability of auto-layout generation given the dimensional category of the room. We conduct our experiments on the proposed real-world interior layout dataset that contains $191208$ designs from the professional designers. Our numerical results demonstrate that the proposed model yields higher-quality layouts in comparison with the state-of-the-art model. The dataset and code are released \href{https://github.com/CODE-SUBMIT/dataset3}{Dataset,Code}