# Imaginarium: Vision-guided High-Quality 3D Scene Layout Generation

arXiv: https://arxiv.org/abs/2510.15564 · HTML: https://mlsanity.com/paper/2510.15564/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 17 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [DeepIM: Deep Iterative Matching for 6D Pose Estimation](https://mlsanity.com/paper/1804.00175/) (2018)
- [ATISS: Autoregressive Transformers for Indoor Scene Synthesis](https://mlsanity.com/paper/2110.03675/) (2021)
- [SceneFormer: Indoor Scene Generation with Transformers](https://mlsanity.com/paper/2012.09793/) (2020)
- [CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion](https://mlsanity.com/paper/2305.16283/) (2023)
- [DiffCAD: Weakly-Supervised Probabilistic CAD Model Retrieval and Alignment from an RGB Image](https://mlsanity.com/paper/2311.18610/) (2023)
- [Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting](https://mlsanity.com/paper/2411.09823/) (2024)
- [Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars](https://mlsanity.com/paper/1704.00112/) (2017)
- [Self-supervised Vision Transformers for 3D Pose Estimation of Novel Objects](https://mlsanity.com/paper/2306.00129/) (2023)
- [Coherent 3D Scene Diffusion From a Single RGB Image](https://mlsanity.com/paper/2412.10294/) (2024)

## Built on by (most cited first, 11 of 11)

- [One Video, One World: Turning Monocular Video into Physical 4D Scenes](https://mlsanity.com/paper/2606.31388/) (2026)
- [MUSE: Agentic 3D Scene Authoring via Memory-Grounded Incremental Requirement Satisfaction](https://mlsanity.com/paper/2606.14168/) (2026)
- [FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute](https://mlsanity.com/paper/2609.08848/) (2026)
- [NaLA: A 3D Native LLM Layout Agent for High-quality 3D Scene Generation](https://mlsanity.com/paper/2606.29395/) (2026)
- [Beyond Placement and Articulation: Usage-Driven Code Scenes for Embodied Interaction](https://mlsanity.com/paper/2608.18840/) (2026)
- [Co-Layout: LLM-driven Co-optimization for Interior Layout](https://mlsanity.com/paper/2511.12474/) (2025)
- [Co-generation of Layout and Shape from Text via Autoregressive 3D Diffusion](https://mlsanity.com/paper/2604.16552/) (2026)
- [JointEdit3D: Feed-Forward 3D Scene Editing in a Unified Latent Space](https://mlsanity.com/paper/2606.13345/) (2026)
- [StateFlow: Building, Evolving, and Accessing 3D World States for Previsualization](https://mlsanity.com/paper/2608.12314/) (2026)
- [HARMONY: Hierarchical Agentic Reasoning for MONocular Image-to-Scene Synthesis](https://mlsanity.com/paper/2609.26793/) (2026)
- [SDesc3D: Towards Layout-Aware 3D Indoor Scene Generation from Short Descriptions](https://mlsanity.com/paper/2604.01972/) (2026)

## Abstract (arXiv)

Generating artistic and coherent 3D scene layouts is crucial in digital content creation. Traditional optimization-based methods are often constrained by cumbersome manual rules, while deep generative models face challenges in producing content with richness and diversity. Furthermore, approaches that utilize large language models frequently lack robustness and fail to accurately capture complex spatial relationships. To address these challenges, this paper presents a novel vision-guided 3D layout generation system. We first construct a high-quality asset library containing 2,037 scene assets and 147 3D scene layouts. Subsequently, we employ an image generation model to expand prompt representations into images, fine-tuning it to align with our asset library. We then develop a robust image parsing module to recover the 3D layout of scenes based on visual semantics and geometric information. Finally, we optimize the scene layout using scene graphs and overall visual semantics to ensure logical coherence and alignment with the images. Extensive user testing demonstrates that our algorithm significantly outperforms existing methods in terms of layout richness and quality. The code and dataset will be available at https://github.com/HiHiAllen/Imaginarium.