HetScene: 不均一性を考慮した拡散モデルによる高密度屋内シーン生成
HetScene: Heterogeneity-Aware Diffusion for Dense Indoor Scene Generation
屋内シーン生成において、物体を主要物体と副次物体に分解し、構造レイアウト生成と文脈レイアウト生成の2段階で生成する不均一性対応フレームワークを提案した。
著者: Zini Chen, Junming Huang, Rong Zhang, Jiamin Xu, Cheng Peng, Chi Wang, Weiwei Xu
分類: cs.CV, cs.AI
原文アブストラクト
Generating controllable and physically plausible indoor scenes is a pivotal prerequisite for constructing high-fidelity simulation environments for embodied AI. However, existing deeplearning-based methods usually treat all objects as homogeneous instances within a unified generation process. While effective for sparse and simplistic layouts, they struggle to model realistic layouts with dense object arrangements and complex spatial dependencies, leadingto limited scalability and degraded physical plausibility. To deal with these challenges, we revisit indoor layout generation from the perspective of structural heterogeneity and decompose the objects into primary objects and secondary objects according to their distinct roles in shaping a scene. Based on this decomposition, we propose HetScene, a heterogeneous two-stage generation framework that decouples indoor layout synthesis into Structural Layout Generation (SLG) and Contextual Layout Generation (CLG). SLG first generates globally coherent structural layouts with only primary objects conditioned on text descriptions, top-down binary room masks, and spatial relation graphs, establishing a stable global macro-skeleton of large core furniture.