# LLplace: The 3D Indoor Scene Layout Generation and Editing via Large Language Model

arXiv: https://arxiv.org/abs/2406.03866 · HTML: https://mlsanity.com/paper/2406.03866/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 30 references that are in the map.
- First posted on arXiv 6 Jun 2024.

## Builds on (most cited first, 27 of 27)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics](https://mlsanity.com/paper/2011.09127/) (2020)
- [ProcTHOR: Large-Scale Embodied AI Using Procedural Generation](https://mlsanity.com/paper/2206.06994/) (2022)
- [An Embodied Generalist Agent in 3D World](https://mlsanity.com/paper/2311.12871/) (2023)
- [LayoutGPT: Compositional Visual Planning and Generation with Large Language Models](https://mlsanity.com/paper/2305.15393/) (2023)
- [3D-FUTURE: 3D Furniture shape with TextURE](https://mlsanity.com/paper/2009.09633/) (2020)
- [Holodeck: Language Guided Generation of 3D Embodied AI Environments](https://mlsanity.com/paper/2312.09067/) (2023)
- [Diffusion-based Generation, Optimization, and Planning in 3D Scenes](https://mlsanity.com/paper/2301.06015/) (2023)
- [LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning](https://mlsanity.com/paper/2311.18651/) (2023)
- [ATISS: Autoregressive Transformers for Indoor Scene Synthesis](https://mlsanity.com/paper/2110.03675/) (2021)
- [Scene-LLM: Extending Language Model for 3D Visual Understanding and Reasoning](https://mlsanity.com/paper/2403.11401/) (2024)
- [SceneFormer: Indoor Scene Generation with Transformers](https://mlsanity.com/paper/2012.09793/) (2020)
- [PhyScene: Physically Interactable 3D Scene Synthesis for Embodied AI](https://mlsanity.com/paper/2404.09465/) (2024)
- [InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior](https://mlsanity.com/paper/2402.04717/) (2024)
- [Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models](https://mlsanity.com/paper/1811.12463/) (2018)
- [MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation](https://mlsanity.com/paper/2308.08239/) (2023)
- [I-Design: Personalized LLM Interior Designer](https://mlsanity.com/paper/2404.02838/) (2024)
- [AlignBench: Benchmarking Chinese Alignment of Large Language Models](https://mlsanity.com/paper/2311.18743/) (2023)
- [Ctrl-Room: Controllable Text-to-3D Room Meshes Generation with Layout Constraints](https://mlsanity.com/paper/2310.03602/) (2023)
- [End-to-End Optimization of Scene Layout](https://mlsanity.com/paper/2007.11744/) (2020)
- [Open-Universe Indoor Scene Generation using LLM Program Synthesis and Uncurated Object Databases](https://mlsanity.com/paper/2403.09675/) (2024)
- [SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation with Fine-Grained Geometry](https://mlsanity.com/paper/2302.10237/) (2023)
- [Scene Synthesis via Uncertainty-Driven Attribute Synchronization](https://mlsanity.com/paper/2108.13499/) (2021)
- [Indoor Scene Generation from a Collection of Semantic-Segmented Depth Images](https://mlsanity.com/paper/2108.09022/) (2021)

## Built on by (most cited first, 30 of 34)

- [Multimodal Spatial Reasoning in the Large Model Era: A Survey and Benchmarks](https://mlsanity.com/paper/2510.25760/) (2025)
- [BlenderGym: Benchmarking Foundational Model Systems for Graphics Editing](https://mlsanity.com/paper/2504.01786/) (2025)
- [Direct Numerical Layout Generation for 3D Indoor Scene Synthesis via Spatial Reasoning](https://mlsanity.com/paper/2506.05341/) (2025)
- [FlairGPT: Repurposing LLMs for Interior Designs](https://mlsanity.com/paper/2501.04648/) (2025)
- [Generative Physical AI in Vision: A Survey](https://mlsanity.com/paper/2501.10928/) (2025)
- [MesaTask: Towards Task-Driven Tabletop Scene Generation via 3D Spatial Reasoning](https://mlsanity.com/paper/2509.22281/) (2025)
- [OptiScene: LLM-driven Indoor Scene Layout Generation via Scaled Human-aligned Data Synthesis and Multi-Stage Preference Optimization](https://mlsanity.com/paper/2506.07570/) (2025)
- [Spatial 3D-LLM: Exploring Spatial Awareness in 3D Vision-Language Models](https://mlsanity.com/paper/2507.16524/) (2025)
- [Video Perception Models for 3D Scene Synthesis](https://mlsanity.com/paper/2506.20601/) (2025)
- [ReSpace: Text-Driven Autoregressive 3D Indoor Scene Synthesis and Editing](https://mlsanity.com/paper/2506.02459/) (2025)
- [Code-as-Room: Generating 3D Rooms from Top-Down View Images via Agentic Code Synthesis](https://mlsanity.com/paper/2605.18451/) (2026)
- [IL3D: A Large-Scale Indoor Layout Dataset for LLM-Driven 3D Scene Generation](https://mlsanity.com/paper/2510.12095/) (2025)
- [ArtiWorld: LLM-Driven Articulation of 3D Objects in Scenes](https://mlsanity.com/paper/2511.12977/) (2025)
- [TabletopGen: Tabletop Scene Generation and Interactive Simulation for Robotic Manipulation](https://mlsanity.com/paper/2512.01204/) (2025)
- [STABLE: Simulation-Ready Tabletop Layout Generation via a Semantics-Physics Dual System](https://mlsanity.com/paper/2605.16137/) (2026)
- [MUSE: Agentic 3D Scene Authoring via Memory-Grounded Incremental Requirement Satisfaction](https://mlsanity.com/paper/2606.14168/) (2026)
- [SceneNAT: Masked Generative Modeling for Language-Guided Indoor Scene Synthesis](https://mlsanity.com/paper/2601.07218/) (2026)
- [SceneCritic: A Symbolic Evaluator for 3D Indoor Scene Synthesis](https://mlsanity.com/paper/2604.13035/) (2026)
- [NaLA: A 3D Native LLM Layout Agent for High-quality 3D Scene Generation](https://mlsanity.com/paper/2606.29395/) (2026)
- [Pair2Scene: Learning Local Object Relations for Procedural Scene Generation](https://mlsanity.com/paper/2604.11808/) (2026)
- [Function2Scene: 3D Indoor Scene Layout from Functional Specifications](https://mlsanity.com/paper/2605.30819/) (2026)
- [3D-DefectBench: A Controlled Factorial Study of Vision-Language Model Evaluation Pipelines for Fine-Grained 3D Generation Defects](https://mlsanity.com/paper/2607.10826/) (2026)
- [HierRelTriple: Guiding Indoor Layout Generation with Hierarchical Relationship Triplet Losses](https://mlsanity.com/paper/2503.20289/) (2025)
- [StructLayoutFormer:Conditional Structured Layout Generation via Structure Serialization and Disentanglement](https://mlsanity.com/paper/2510.26141/) (2025)
- [ReLayout: Versatile and Structure-Preserving Design Layout Editing via Relation-Aware Design Reconstruction](https://mlsanity.com/paper/2602.01046/) (2026)
- [LLM-Driven 3D Scene Generation of Agricultural Simulation Environments](https://mlsanity.com/paper/2602.11706/) (2026)
- [Text-to-Stage: Spatial Layouts from Long-form Narratives](https://mlsanity.com/paper/2603.17832/) (2026)
- [Orchestrating Spatial Semantics via a Zone-Graph Paradigm for Intricate Indoor Scene Generation](https://mlsanity.com/paper/2605.02537/) (2026)
- [R3L: Reasoning 3D Layouts from Relative Spatial Relations](https://mlsanity.com/paper/2605.06758/) (2026)
- [HomeWorld: A Unified Floorplan-to-Furnished Framework for Generating Controllable, Densely Interactive Whole-Home Scenes](https://mlsanity.com/paper/2606.06390/) (2026)

## Abstract (arXiv)

Designing 3D indoor layouts is a crucial task with significant applications in virtual reality, interior design, and automated space planning. Existing methods for 3D layout design either rely on diffusion models, which utilize spatial relationship priors, or heavily leverage the inferential capabilities of proprietary Large Language Models (LLMs), which require extensive prompt engineering and in-context exemplars via black-box trials. These methods often face limitations in generalization and dynamic scene editing. In this paper, we introduce LLplace, a novel 3D indoor scene layout designer based on lightweight fine-tuned open-source LLM Llama3. LLplace circumvents the need for spatial relationship priors and in-context exemplars, enabling efficient and credible room layout generation based solely on user inputs specifying the room type and desired objects. We curated a new dialogue dataset based on the 3D-Front dataset, expanding the original data volume and incorporating dialogue data for adding and removing objects. This dataset can enhance the LLM's spatial understanding. Furthermore, through dialogue, LLplace activates the LLM's capability to understand 3D layouts and perform dynamic scene editing, enabling the addition and removal of objects. Our approach demonstrates that LLplace can effectively generate and edit 3D indoor layouts interactively and outperform existing methods in delivering high-quality 3D design solutions. Code and dataset will be released.