# SDesc3D: Towards Layout-Aware 3D Indoor Scene Generation from Short Descriptions

arXiv: https://arxiv.org/abs/2604.01972 · HTML: https://mlsanity.com/paper/2604.01972/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 2 Apr 2026.

## Builds on (most cited first, 24 of 24)

- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [Holodeck: Language Guided Generation of 3D Embodied AI Environments](https://mlsanity.com/paper/2312.09067/) (2023)
- [DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis](https://mlsanity.com/paper/2303.14207/) (2023)
- [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)
- [Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generation](https://mlsanity.com/paper/2505.02836/) (2025)
- [Deep Generative Modeling for Scene Synthesis via Hybrid Representations](https://mlsanity.com/paper/1808.02084/) (2018)
- [SceneWeaver: All-in-One 3D Scene Synthesis with an Extensible and Self-Reflective Agent](https://mlsanity.com/paper/2509.20414/) (2025)
- [CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion](https://mlsanity.com/paper/2305.16283/) (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)
- [Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting](https://mlsanity.com/paper/2411.09823/) (2024)
- [Global-Local Tree Search in VLMs for 3D Indoor Scene Generation](https://mlsanity.com/paper/2503.18476/) (2025)
- [Direct Numerical Layout Generation for 3D Indoor Scene Synthesis via Spatial Reasoning](https://mlsanity.com/paper/2506.05341/) (2025)
- [FirePlace: Geometric Refinements of LLM Common Sense Reasoning for 3D Object Placement](https://mlsanity.com/paper/2503.04919/) (2025)
- [HSM: Hierarchical Scene Motifs for Multi-Scale Indoor Scene Generation](https://mlsanity.com/paper/2503.16848/) (2025)
- [SPATIALGEN: Layout-guided 3D Indoor Scene Generation](https://mlsanity.com/paper/2509.14981/) (2025)
- [Graph Canvas for Controllable 3D Scene Generation](https://mlsanity.com/paper/2412.00091/) (2024)
- [Imaginarium: Vision-guided High-Quality 3D Scene Layout Generation](https://mlsanity.com/paper/2510.15564/) (2025)
- [Text-to-Scene with Large Reasoning Models](https://mlsanity.com/paper/2509.26091/) (2025)
- [DisCo-Layout: Disentangling and Coordinating Semantic and Physical Refinement in a Multi-Agent Framework for 3D Indoor Layout Synthesis](https://mlsanity.com/paper/2510.02178/) (2025)
- [Learning to Place Objects with Programs and Iterative Self Training](https://mlsanity.com/paper/2503.04496/) (2025)
- [SceneLCM: End-to-End Layout-Guided Interactive Indoor Scene Generation with Latent Consistency Model](https://mlsanity.com/paper/2506.07091/) (2025)

## Abstract (arXiv)

3D indoor scene generation conditioned on short textual descriptions provides a promising avenue for interactive 3D environment construction without the need for labor-intensive layout specification. Despite recent progress in text-conditioned 3D scene generation, existing works suffer from poor physical plausibility and insufficient detail richness in such semantic condensation cases, largely due to their reliance on explicit semantic cues about compositional objects and their spatial relationships. This limitation highlights the need for enhanced 3D reasoning capabilities, particularly in terms of prior integration and spatial anchoring. Motivated by this, we propose SDesc3D, a short-text conditioned 3D indoor scene generation framework, that leverages multi-view structural priors and regional functionality implications to enable 3D layout reasoning under sparse textual guidance. Specifically, we introduce a Multi-view scene prior augmentation that enriches underspecified textual inputs with aggregated multi-view structural knowledge, shifting from inaccessible semantic relation cues to multi-view relational prior aggregation. Building on this, we design a Functionality-aware layout grounding, employing regional functionality grounding for implicit spatial anchors and conducting hierarchical layout reasoning to enhance scene organization and semantic plausibility. Furthermore, an Iterative reflection-rectification scheme is employed for progressive structural plausibility refinement via self-rectification. Extensive experiments show that our method outperforms existing approaches on short-text conditioned 3D indoor scene generation. Code will be publicly available.