Nestwork：基于潜变量异构图扩散的条件式三维带家具住宅布局生成
Nestwork: Conditional 3D Furnished House Layout Generation through Latent Heterogeneous Graph Diffusion
把整栋房子的房间与家具建成带类型节点和多种空间关系的异构图，用单一异构图注意力自编码器压到紧凑潜空间、再在其上做扩散去噪，这种「图→潜变量→扩散」的范式可直接迁移到场景图驱动的 3D 室内合成与布局补全；随机掩码训练让同一模型支持从仅拓扑到全标注的多种条件强度，对需要灵活用户控制的生成任务很有参考价值。
原文依据 A single unconditional autoencoder based on a heterogeneous graph attention network embeds this graph into a compact latent space
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文提出 Nestwork，一个统一的潜扩散框架，利用由房间与家具构成的异构图进行条件式三维带家具住宅布局生成。设计合理且可控、并能反映住宅底层语义结构的三维布局，是 AI 辅助建筑设计中的关键挑战。现有的基于图的方法要么生成不带家具的多房间布局，要么逐个房间地生成带家具场景，因而无法对房间结构与家具摆放进行联合推理。Nestwork 将整栋住宅表示为一个异构图，其中包含带类型的房间节点与家具节点以及多种空间关系。一个基于异构图注意力网络的单一无条件自编码器将该图嵌入紧凑的潜空间，并由一个低秩关系场在测试时补偿缺失的几何边信息。扩散去噪器通过随机掩码训练一次，使同一模型能够在不同条件强度下运行，从仅含拓扑的图到完全标注的图。多级条件将掩码的节点级注意力与图级嵌入相结合，以支持灵活的用户控制，包括通过自然语言描述指定的布局。在 3D-FRONT 数据集上的实验表明，Nestwork 实现了高保真度、结构一致性与多样性。受控消融实验进一步验证了各组成部分的贡献。
英文原文摘要 1453 字符
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.