← 研踪 PaperTrailCVF · CVPR 2026:cvf:c45ad46e4dabbb17b1c08bdfc92e31fa · 记录 #1
Nestwork: Conditional 3D Furnished House Layout Generation through Latent Heterogeneous Graph Diffusion
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 02:47
摘要
This paper introduces Nestwork, a unified latent-diffusion framework for conditional 3D furnished house layout generation using a heterogeneous graph of rooms and furniture. Designing reasonable and controllable 3D layouts that reflect the underlying semantic structure of a house is a key challenge in AI-assisted architectural design. Existing graph-based methods either produce unfurnished multi-room layouts or generate furnished scenes one room at a time, preventing joint reasoning over room structure and furniture placement. Nestwork represents an entire house as a heterogeneous graph with typed room and furniture nodes and multiple spatial relations. A single unconditional autoencoder based on a heterogeneous graph attention network embeds this graph into a compact latent space, and a low-rank relational field compensates for missing geometric edge information at test time. A diffusion denoiser is trained once using random masking, enabling the same model to operate under different conditioning strengths, from topology-only to fully annotated graphs. Multi-level conditioning combines masked node-level attention with graph-level embeddings to support flexible user control, including layouts specified through natural-language descriptions. Experiments on the 3D-FRONT dataset show that Nestwork achieves high fidelity, structural consistency, and diversity. Controlled ablations further validate the contributions of each component.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026 · 最新观测版本
Nestwork: Conditional 3D Furnished House Layout Generation through Latent Heterogeneous Graph Diffusion
This paper introduces Nestwork, a unified latent-diffusion framework for conditional 3D furnished house layout generation using a heterogeneous graph of rooms and furniture. Designing reasonable and controllable 3D layouts that reflect the underlying semantic structure of a house is a key challenge in AI-assisted architectural design. Existing graph-based methods either produce unfurnished multi-room layouts or generate furnished scenes one room at a time, preventing joint reasoning over room structure and furniture placement. Nestwork represents an entire house as a heterogeneous graph with typed room and furniture nodes and multiple spatial relations. A single unconditional autoencoder based on a heterogeneous graph attention network embeds this graph into a compact latent space, and a low-rank relational field compensates for missing geometric edge information at test time. A diffusion denoiser is trained once using random masking, enabling the same model to operate under different conditioning strengths, from topology-only to fully annotated graphs. Multi-level conditioning combines masked node-level attention with graph-level embeddings to support flexible user control, including layouts specified through natural-language descriptions. Experiments on the 3D-FRONT dataset show that Nestwork achieves high fidelity, structural consistency, and diversity. Controlled ablations further validate the contributions of each component.
查看此版本解读 →查看此版本原文 ↗