Deep Layout of Custom-size Furniture through Multiple-domain Learning
This work provides an incremental improvement in automated interior design for professional interior designers by enabling custom-sized furniture layouts.
This paper addresses the problem of automatically generating custom-sized furniture layouts for interior scenes. The proposed model, trained on a dataset of 710,700 professional designs, produces higher-quality layouts compared to state-of-the-art models.
In this paper, we propose a multiple-domain model for producing a custom-size furniture layout in the interior scene. This model is aimed to support professional interior designers to produce interior decoration solutions with custom-size furniture more quickly. The proposed model combines a deep layout module, a domain attention module, a dimensional domain transfer module, and a custom-size module in the end-end training. Compared with the prior work on scene synthesis, our proposed model enhances the ability of auto-layout of custom-size furniture in the interior room. We conduct our experiments on a real-world interior layout dataset that contains $710,700$ designs from professional designers. Our numerical results demonstrate that the proposed model yields higher-quality layouts of custom-size furniture in comparison with the state-of-art model.