Controllable scene synthesis aims to create interactive environments for various industrial use cases. Scene graphs provide a highly suitable interface to facilitate these applications by abstracting the scene context in a compact manner. Existing methods, reliant on retrieval from extensive databases or pre-trained shape embeddings, often overlook scene-object and object-object relationships, leading to inconsistent results due to their limited generation capacity. To address this issue, we present CommonScenes, a fully generative model that converts scene graphs into corresponding controllable 3D scenes, which are semantically realistic and conform to commonsense. Our pipeline consists of two branches, one predicting the overall scene layout via a variational auto-encoder and the other generating compatible shapes via latent diffusion, capturing global scene-object and local inter-object relationships while preserving shape diversity. The generated scenes can be manipulated by editing the input scene graph and sampling the noise in the diffusion model. Due to lacking a scene graph dataset offering high-quality object-level meshes with relations, we also construct SG-FRONT, enriching the off-the-shelf indoor dataset 3D-FRONT with additional scene graph labels. Extensive experiments are conducted on SG-FRONT where CommonScenes shows clear advantages over other methods regarding generation consistency, quality, and diversity. Codes and the dataset will be released upon acceptance.

翻译：可控场景合成旨在为多种工业应用创建交互式环境。场景图作为一种高度适配的接口，能以紧凑方式抽象场景上下文，从而支持这些应用。现有方法依赖于从大型数据库检索或预训练的形状嵌入，常忽略场景-物体与物体-物体间的关系，因生成能力受限而导致结果不一致。为解决该问题，我们提出CommonScenes——一种全生成式模型，可将场景图转换为对应的可控三维场景，这些场景在语义上真实且符合常识。我们的流程包含两个分支：一个通过变分自编码器预测整体场景布局，另一个通过潜在扩散模型生成兼容形状，在保留形状多样性的同时捕获全局场景-物体关系与局部物体-物体关系。生成的场景可通过编辑输入场景图及对扩散模型中的噪声进行采样来操控。由于目前缺乏提供高质量物体级关联网格的场景图数据集，我们还构建了SG-FRONT，在现有室内数据集3D-FRONT中补充了额外的场景图标签。在SG-FRONT上开展的广泛实验表明，CommonScenes在生成一致性、质量与多样性方面均显著优于其他方法。代码与数据集将在论文被接收后发布。