RoomDesigner: Encoding Anchor-latents for Style-consistent and
  Shape-compatible Indoor Scene Generation
Yiqun Zhao Zibo Zhao Jing Li Sixun Dong Shenghua Gao
Indoor scene generation aims at creating shape-compatible, style-consistent
furniture arrangements within a spatially reasonable layout. However, most
existing approaches primarily focus on generating plausible furniture layouts
without incorporating specific details related to individual furniture pieces.
To address this limitation, we propose a two-stage model integrating shape
priors into the indoor scene generation by encoding furniture as anchor latent
representations. In the first stage, we employ discrete vector quantization to
encode furniture pieces as anchor-latents. Based on the anchor-latents
representation, the shape and location information of the furniture was
characterized by a concatenation of location, size, orientation, class, and our
anchor latent. In the second stage, we leverage a transformer model to predict
indoor scenes autoregressively. Thanks to incorporating the proposed
anchor-latents representations, our generative model produces shape-compatible
and style-consistent furniture arrangements and synthesis furniture in diverse
shapes. Furthermore, our method facilitates various human interaction
applications, such as style-consistent scene completion, object mismatch
correction, and controllable object-level editing. Experimental results on the
3D-Front dataset demonstrate that our approach can generate more consistent and
compatible indoor scenes compared to existing methods, even without shape
retrieval. Additionally, extensive ablation studies confirm the effectiveness
of our design choices in the indoor scene generation model.