MaskScene: Hierarchical Conditional Masked Models for Real-Time 3D Indoor Scene Synthesis

Indoor scene synthesis is essential for creative industries. Driven by this demand, recent advances in scene synthesis using diffusion and autoregressive models have shown promising results. However, existing models struggle to achieve real-time performance, high visual fidelity, and flexible scene editing simultaneously. To tackle this challenge, we propose MaskScene, a novel hierarchical conditional masked model for real-time 3D indoor scene synthesis and editing. Specifically, MaskScene introduces a hierarchical scene representation that explicitly encodes scene relationships, semantics, and tokens. Based on this representation, we design a hierarchical conditional masked modeling architecture that enables parallel and iterative decoding, conditioned on both semantics and relationships. MaskScene leverages local object masking and hierarchical scene structures to infer occluded regions from partial observations, enabling rapid construction of 3D indoor environments that accurately reflect real-world scenes. Compared to state-of-the-art methods, MaskScene achieves 80× faster generation speed and improves scene quality by 10%, while also supporting zero-shot editing, such as scene completion and rearrangement, without extra fine-tuning.