NaLA: A 3D Native LLM Layout Agent for High-quality 3D Scene Generation

Recently, Large Language Models (LLMs) have emerged aspromising layout agents for 3D scene generation. Existing layout agentsstill suffer from implausible layout generation because most of them con-vert 3D assets and 3D layouts into textual descriptions as inputs andoutputs, which involves severe information loss due to the modality gapbetween texts and 3D assets and 3D layouts. We propose NaLA, a native3D LLM layout Agent for high-quality 3D scene generation by placing 3Dassets in the scene. For the inputs, NaLA encodes 3D scene boundariesand 3D assets directly into the LLM, preserving fine-grained geometryand enabling explicit reasoning over relationships like collisions, surfacesupporting, and containment. To accurately output the positions and ori-entations of assets, NaLA adopts a coarse-to-fine prediction mechanismthat first predicts discrete poses in an autoregressive manner and thenrefines the discrete poses with a continuous regression. Trained on diverselayout datasets, NaLA attains strong geometric perception and layout co-herence. Experiments demonstrate that NaLA outperforms prior layoutagents in both generation quality and inference efficiency, with compre-hensive ablation studies to verify each component’s effectiveness.