# Human-centric Indoor Scene Synthesis Using Stochastic Grammar

arXiv: https://arxiv.org/abs/1808.08473 · HTML: https://mlsanity.com/paper/1808.08473/

## Key facts

- Cited by 74 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2018.

## Built on by (most cited first, 30 of 74)

- [Diffusion-based Generation, Optimization, and Planning in 3D Scenes](https://mlsanity.com/paper/2301.06015/) (2023)
- [ATISS: Autoregressive Transformers for Indoor Scene Synthesis](https://mlsanity.com/paper/2110.03675/) (2021)
- [Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds](https://mlsanity.com/paper/2109.00179/) (2021)
- [DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis](https://mlsanity.com/paper/2303.14207/) (2023)
- [Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generation](https://mlsanity.com/paper/2406.11824/) (2024)
- [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)
- [I-Design: Personalized LLM Interior Designer](https://mlsanity.com/paper/2404.02838/) (2024)
- [AnyHome: Open-Vocabulary Generation of Structured and Textured 3D Homes](https://mlsanity.com/paper/2312.06644/) (2023)
- [LEGO-Net: Learning Regular Rearrangements of Objects in Rooms](https://mlsanity.com/paper/2301.09629/) (2023)
- [BLT: Bidirectional Layout Transformer for Controllable Layout Generation](https://mlsanity.com/paper/2112.05112/) (2021)
- [MIME: Human-Aware 3D Scene Generation](https://mlsanity.com/paper/2212.04360/) (2022)
- [SceneWeaver: All-in-One 3D Scene Synthesis with an Extensible and Self-Reflective Agent](https://mlsanity.com/paper/2509.20414/) (2025)
- [Open-Universe Indoor Scene Generation using LLM Program Synthesis and Uncurated Object Databases](https://mlsanity.com/paper/2403.09675/) (2024)
- [PhyRecon: Physically Plausible Neural Scene Reconstruction](https://mlsanity.com/paper/2404.16666/) (2024)
- [CG3D: Compositional Generation for Text-to-3D via Gaussian Splatting](https://mlsanity.com/paper/2311.17907/) (2023)
- [SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation with Fine-Grained Geometry](https://mlsanity.com/paper/2302.10237/) (2023)
- [AnySkill: Learning Open-Vocabulary Physical Skill for Interactive Agents](https://mlsanity.com/paper/2403.12835/) (2024)
- [Mixed Diffusion for 3D Indoor Scene Synthesis](https://mlsanity.com/paper/2405.21066/) (2024)
- [Indoor Scene Generation from a Collection of Semantic-Segmented Depth Images](https://mlsanity.com/paper/2108.09022/) (2021)
- [Learning 3D Scene Priors with 2D Supervision](https://mlsanity.com/paper/2211.14157/) (2022)
- [HSM: Hierarchical Scene Motifs for Multi-Scale Indoor Scene Generation](https://mlsanity.com/paper/2503.16848/) (2025)
- [3D Scene Generation: A Survey](https://mlsanity.com/paper/2505.05474/) (2025)
- [DiffInDScene: Diffusion-based High-Quality 3D Indoor Scene Generation](https://mlsanity.com/paper/2306.00519/) (2023)
- [Chat2Layout: Interactive 3D Furniture Layout with a Multimodal LLM](https://mlsanity.com/paper/2407.21333/) (2024)
- [LayoutEnhancer: Generating Good Indoor Layouts from Imperfect Data](https://mlsanity.com/paper/2202.00185/) (2022)
- [Sequential Manipulation Planning on Scene Graph](https://mlsanity.com/paper/2207.04364/) (2022)
- [CLIP-Layout: Style-Consistent Indoor Scene Synthesis with Semantic Furniture Embedding](https://mlsanity.com/paper/2303.03565/) (2023)
- [Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignments](https://mlsanity.com/paper/2103.16095/) (2021)
- [SimJEB: Simulated Jet Engine Bracket Dataset](https://mlsanity.com/paper/2105.03534/) (2021)

## Abstract (arXiv)

We present a human-centric method to sample and synthesize 3D room layouts and 2D images thereof, to obtain large-scale 2D/3D image data with perfect per-pixel ground truth. An attributed spatial And-Or graph (S-AOG) is proposed to represent indoor scenes. The S-AOG is a probabilistic grammar model, in which the terminal nodes are object entities. Human contexts as contextual relations are encoded by Markov Random Fields (MRF) on the terminal nodes. We learn the distributions from an indoor scene dataset and sample new layouts using Monte Carlo Markov Chain. Experiments demonstrate that our method can robustly sample a large variety of realistic room layouts based on three criteria: (i) visual realism comparing to a state-of-the-art room arrangement method, (ii) accuracy of the affordance maps with respect to groundtruth, and (ii) the functionality and naturalness of synthesized rooms evaluated by human subjects. The code is available at https://github.com/SiyuanQi/human-centric-scene-synthesis.