# SceneFoundry: Generating Interactive Infinite 3D Worlds

arXiv: https://arxiv.org/abs/2601.05810 · HTML: https://mlsanity.com/paper/2601.05810/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 9 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [3D-FUTURE: 3D Furniture shape with TextURE](https://mlsanity.com/paper/2009.09633/) (2020)
- [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)
- [GenAug: Retargeting behaviors to unseen situations via Generative Augmentation](https://mlsanity.com/paper/2302.06671/) (2023)
- [S-INF: Towards Realistic Indoor Scene Synthesis via Scene Implicit Neural Field](https://mlsanity.com/paper/2412.17561/) (2024)

## Built on by (most cited first, 5 of 5)

- [REST3D: Reconstructing Physically Stable 3D Scenes from a Single Image](https://mlsanity.com/paper/2605.30338/) (2026)
- [SceneCritic: A Symbolic Evaluator for 3D Indoor Scene Synthesis](https://mlsanity.com/paper/2604.13035/) (2026)
- [3D Generation for Embodied AI and Robotic Simulation: A Survey](https://mlsanity.com/paper/2604.26509/) (2026)
- [VoxScene: Anchor-Conditioned Voxel Diffusion for Indoor Scene Arrangement](https://mlsanity.com/paper/2605.17102/) (2026)
- [SMART: Zero-Shot Sim-to-Real Articulated Object Manipulation via Large-Scale Synthetic Pretraining](https://mlsanity.com/paper/2610.07652/) (2026)

## Abstract (arXiv)

The ability to automatically generate large-scale, interactive, and physically realistic 3D environments is crucial for advancing robotic learning and embodied intelligence. However, existing generative approaches often fail to capture the functional complexity of real-world interiors, particularly those containing articulated objects with movable parts essential for manipulation and navigation. This paper presents SceneFoundry, a language-guided diffusion framework that generates apartment-scale 3D worlds with functionally articulated furniture and semantically diverse layouts for robotic training. From natural language prompts, an LLM module controls floor layout generation, while diffusion-based posterior sampling efficiently populates the scene with articulated assets from large-scale 3D repositories. To ensure physical usability, SceneFoundry employs differentiable guidance functions to regulate object quantity, prevent articulation collisions, and maintain sufficient walkable space for robotic navigation. Extensive experiments demonstrate that our framework generates structurally valid, semantically coherent, and functionally interactive environments across diverse scene types and conditions, enabling scalable embodied AI research. project page: https://anc891203.github.io/SceneFoundry-Demo/