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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.27555 |
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| _version_ | 1866914520225546240 |
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| author | Tang, Song Zhao, Kaiyong Li, Yuliang Yan, Qingsong Sun, Penglei Zou, Junyi Wang, Qiang Chu, Xiaowen |
| author_facet | Tang, Song Zhao, Kaiyong Li, Yuliang Yan, Qingsong Sun, Penglei Zou, Junyi Wang, Qiang Chu, Xiaowen |
| contents | Automatically generating interactive 3D indoor scenes from natural language is crucial for virtual reality, gaming, and embodied AI. However, existing LLM-based approaches often suffer from spatial errors and collisions, in part because common scene representations-raw coordinates or verbose code-are difficult for models to reason about 3D spatial relationships and physical constraints. We propose SpatialGrammar, a domain-specific language that represents gravity-aligned indoor layouts as BEV grid placements with deterministic compilation to valid 3D geometry, enabling verifiable constraint checking. Building on this representation, we develop (1) SG-Agent, a closed-loop system that uses compiler feedback to iteratively refine scenes and enforce collision constraints, and (2) SG-Mini, a 104M-parameter model trained entirely on compiler-validated synthetic data. Across 159 test scenes spanning five scenarios of different complexity, SG-Agent improves spatial fidelity and physical plausibility over prior methods, while SG-Mini performs competitively against larger LLM-based baselines on single-shot generation scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_27555 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SpatialGrammar: A Domain-Specific Language for LLM-Based 3D Indoor Scene Generation Tang, Song Zhao, Kaiyong Li, Yuliang Yan, Qingsong Sun, Penglei Zou, Junyi Wang, Qiang Chu, Xiaowen Artificial Intelligence Automatically generating interactive 3D indoor scenes from natural language is crucial for virtual reality, gaming, and embodied AI. However, existing LLM-based approaches often suffer from spatial errors and collisions, in part because common scene representations-raw coordinates or verbose code-are difficult for models to reason about 3D spatial relationships and physical constraints. We propose SpatialGrammar, a domain-specific language that represents gravity-aligned indoor layouts as BEV grid placements with deterministic compilation to valid 3D geometry, enabling verifiable constraint checking. Building on this representation, we develop (1) SG-Agent, a closed-loop system that uses compiler feedback to iteratively refine scenes and enforce collision constraints, and (2) SG-Mini, a 104M-parameter model trained entirely on compiler-validated synthetic data. Across 159 test scenes spanning five scenarios of different complexity, SG-Agent improves spatial fidelity and physical plausibility over prior methods, while SG-Mini performs competitively against larger LLM-based baselines on single-shot generation scenarios. |
| title | SpatialGrammar: A Domain-Specific Language for LLM-Based 3D Indoor Scene Generation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.27555 |