# FlairGPT: Repurposing LLMs for Interior Designs

arXiv: https://arxiv.org/abs/2501.04648 · HTML: https://mlsanity.com/paper/2501.04648/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 43 references that are in the map.
- First posted on arXiv 8 Jan 2025.

## Builds on (most cited first, 21 of 21)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024)
- [3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics](https://mlsanity.com/paper/2011.09127/) (2020)
- [LayoutGPT: Compositional Visual Planning and Generation with Large Language Models](https://mlsanity.com/paper/2305.15393/) (2023)
- [Holodeck: Language Guided Generation of 3D Embodied AI Environments](https://mlsanity.com/paper/2312.09067/) (2023)
- [ATISS: Autoregressive Transformers for Indoor Scene Synthesis](https://mlsanity.com/paper/2110.03675/) (2021)
- [Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models](https://mlsanity.com/paper/1811.12463/) (2018)
- [I-Design: Personalized LLM Interior Designer](https://mlsanity.com/paper/2404.02838/) (2024)
- [Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey](https://mlsanity.com/paper/2404.01869/) (2024)
- [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)
- [LLplace: The 3D Indoor Scene Layout Generation and Editing via Large Language Model](https://mlsanity.com/paper/2406.03866/) (2024)
- [Indoor and Outdoor 3D Scene Graph Generation via Language-Enabled Spatial Ontologies](https://mlsanity.com/paper/2312.11713/) (2023)
- [LayoutEnhancer: Generating Good Indoor Layouts from Imperfect Data](https://mlsanity.com/paper/2202.00185/) (2022)
- [Tell2Design: A Dataset for Language-Guided Floor Plan Generation](https://mlsanity.com/paper/2311.15941/) (2023)
- [Fast and Scalable Position-Based Layout Synthesis](https://mlsanity.com/paper/1809.10526/) (2018)

## Built on by (most cited first, 24 of 24)

- [HSM: Hierarchical Scene Motifs for Multi-Scale Indoor Scene Generation](https://mlsanity.com/paper/2503.16848/) (2025)
- [Large Language Models for Computer-Aided Design: A Survey](https://mlsanity.com/paper/2505.08137/) (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)
- [M3DLayout: A Multi-Source Dataset of 3D Indoor Layouts and Structured Descriptions for 3D Generation](https://mlsanity.com/paper/2509.23728/) (2025)
- [From Programs to Poses: Factored Real-World Scene Generation via Learned Program Libraries](https://mlsanity.com/paper/2510.10292/) (2025)
- [ShapeLib: Designing a library of programmatic 3D shape abstractions with Large Language Models](https://mlsanity.com/paper/2502.08884/) (2025)
- [AnchoredDream: Zero-Shot 360deg Indoor Scene Generation from a Single View via Geometric Grounding](https://mlsanity.com/paper/2601.16532/) (2026)
- [HLG: Comprehensive 3D Room Construction via Hierarchical Layout Generation](https://mlsanity.com/paper/2508.17832/) (2025)
- [SMooGPT: Stylized Motion Generation using Large Language Models](https://mlsanity.com/paper/2509.04058/) (2025)
- [ShapeCraft: LLM Agents for Structured, Textured and Interactive 3D Modeling](https://mlsanity.com/paper/2510.17603/) (2025)
- [STABLE: Simulation-Ready Tabletop Layout Generation via a Semantics-Physics Dual System](https://mlsanity.com/paper/2605.16137/) (2026)
- [Seeing Through Clutter: Structured 3D Scene Reconstruction via Iterative Object Removal](https://mlsanity.com/paper/2602.04053/) (2026)
- [SceneCritic: A Symbolic Evaluator for 3D Indoor Scene Synthesis](https://mlsanity.com/paper/2604.13035/) (2026)
- [Co-Layout: LLM-driven Co-optimization for Interior Layout](https://mlsanity.com/paper/2511.12474/) (2025)
- [BIM-Native Tokenization for Constraint-Aware Room Layout Synthesis](https://mlsanity.com/paper/2512.04832/) (2025)
- [HOG-Layout: Hierarchical 3D Scene Generation, Optimization and Editing via Vision-Language Models](https://mlsanity.com/paper/2604.10772/) (2026)
- [CasLayout: Cascaded 3D Layout Diffusion for Indoor Scene Synthesis with Implicit Relation Modeling](https://mlsanity.com/paper/2604.27361/) (2026)
- [Aggregating LLM-Based Weak Verifiers for Spatial Layout Generation](https://mlsanity.com/paper/2606.05268/) (2026)
- [iARCS: Iterative Agentic RL for Controllable 3D Scene Generation](https://mlsanity.com/paper/2608.06161/) (2026)
- [FilmSceneDesigner: Chaining Set Design for Procedural Film Scene Generation](https://mlsanity.com/paper/2511.19137/) (2025)
- [Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments](https://mlsanity.com/paper/2607.02407/) (2026)
- [Learning an Interior Layout Policy in a Domain Specific Language Action Space](https://mlsanity.com/paper/2608.07547/) (2026)
- [Task-Adaptive Grounded 3D-Programmers Using 2D VLMs](https://mlsanity.com/paper/2610.02021/) (2026)
- [Co-DSL: Human-LLM Co-design of Reusable Spatial Concepts for Layout Generation](https://mlsanity.com/paper/2610.15290/) (2026)

## Abstract (arXiv)

Interior design involves the careful selection and arrangement of objects to create an aesthetically pleasing, functional, and harmonized space that aligns with the client's design brief. This task is particularly challenging, as a successful design must not only incorporate all the necessary objects in a cohesive style, but also ensure they are arranged in a way that maximizes accessibility, while adhering to a variety of affordability and usage considerations. Data-driven solutions have been proposed, but these are typically room- or domain-specific and lack explainability in their design design considerations used in producing the final layout. In this paper, we investigate if large language models (LLMs) can be directly utilized for interior design. While we find that LLMs are not yet capable of generating complete layouts, they can be effectively leveraged in a structured manner, inspired by the workflow of interior designers. By systematically probing LLMs, we can reliably generate a list of objects along with relevant constraints that guide their placement. We translate this information into a design layout graph, which is then solved using an off-the-shelf constrained optimization setup to generate the final layouts. We benchmark our algorithm in various design configurations against existing LLM-based methods and human designs, and evaluate the results using a variety of quantitative and qualitative metrics along with user studies. In summary, we demonstrate that LLMs, when used in a structured manner, can effectively generate diverse high-quality layouts, making them a viable solution for creating large-scale virtual scenes. Project webpage at https://flairgpt.github.io/