GALA: Geometry-Aware Language Model for Controllable Object Arrangement

Text-driven 3D indoor scene generation has advanced from dataset-bound layout modeling to open-vocabulary synthesis with large language and vision-language models. Yet existing methods remain limited: one-pass generators often yield geometrically invalid layouts, heavy post-hoc optimization is costly and unstable, and prompt-only planners lack reusable layout priors for functional grouping and object relations. We propose ScenePilot, a retrieval-augmented Grow-and-Repair framework that formulates scene generation as prior-guided incremental growth with learned rectification. Given a prompt, the Hierarchical Retrieval-Augmented Planning (HRAP) module retrieves room-, group-, and anchor-level layout priors to support functional group planning. A text-driven base generator then inserts object groups sequentially, while the Reinforcement Multimodal Repair (RMR) module performs lightweight local correction after each insertion and a final global repair after completion. To train this policy, we construct SceneReverse-17k, a repair-trajectory dataset built by perturbing high-quality 3D scenes in position, rotation, and scale, then using inverse operations as executable rectification targets. The policy predicts structured move--rotate--scale actions from rendered views, scene state, retrieved priors, and edit history. By combining HRAP with RMR, ScenePilot offers an efficient alternative to one-shot generation and heavy full-scene optimization, improving physical plausibility, functional coherence, and controllability while preserving diversity.

We introduce ReRef-3D, a benchmark for language-guided single-object placement in controlled 3D tabletop scenes. It contains 33,826 instructions across 998 CLEVR-derived scenes, spanning 16 placement types and direct, one-hop, and two-hop references. Given that an instruction defines a region of acceptable placements rather than one coordinate, predictions are evaluated by relation satisfaction and physical validity. Each instruction also includes a verified natural rephrasing. We evaluate LLaVA-3D, 3D-LLM, 3D-LLaVA, LL3DA, and PlaceIt3D, with PlaceIt3D evaluated under both point-based and region-based supervision. Region-supervised PlaceIt3D achieves the highest placement accuracy at about 80% and LLaVA-3D reaches about 70%. Performance varies by relation type, but nearest and aligned remain the most difficult. Rephrasing has little effect on performance overall.

Recently, Large Language Models (LLMs) have emerged as promising layout agents for 3D scene generation. Existing layout agents still suffer from implausible layout generation because most of them convert 3D assets and 3D layouts into textual descriptions as inputs and outputs, which involves severe information loss due to the modality gap between texts and 3D assets and 3D layouts. We propose NaLA, a native 3D LLM layout Agent for high-quality 3D scene generation by placing 3D assets in the scene. For the inputs, NaLA encodes 3D scene boundaries and 3D assets directly into the LLM, preserving fine-grained geometry and enabling explicit reasoning over relationships like collisions, surface supporting, and containment. To accurately output the positions and orientations of assets, NaLA adopts a coarse-to-fine prediction mechanism that first predicts discrete poses in an autoregressive manner and then refines the discrete poses with a continuous regression. Trained on diverse layout datasets, NaLA attains strong geometric perception and layout coherence. Experiments demonstrate that NaLA outperforms prior layout agents in both generation quality and inference efficiency, with comprehensive ablation studies to verify each component's effectiveness.