manycore-research/SpatialGen-1.0
SpatialGen: Layout-guided 3D Indoor Scene Generation
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<div align="center"> <picture> <source srcset="https://cdn-uploads.huggingface.co/production/uploads/6437c0ead38ce48bdd4b0067/myrWYVNd4m-DuxV39VQZ0.png" media="(prefers-color-scheme: dark)"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6437c0ead38ce48bdd4b0067/QQvDtmokH4ZjwH0wppqFC.png" width="60%" alt="SpatialLM""/> </picture> </div> <hr style="margin-top: 0; margin-bottom: 8px;"> <div align="center" style="margin-top: 0; padding-top: 0; line-height: 1;"> <a href="https://manycore-research.github.io/SpatialGen" target="blank" style="margin: 2px;"><img alt="Project" src="https://img.shields.io/badge/🌐%20Project-SpatialGen-ffc107?color=42a5f5&logoColor=white" style="display: inline-block; vertical-align: middle;"/></a> <a href="https://arxiv.org/abs/2509.14981" target="blank" style="margin: 2px;"><img alt="arXiv" src="https://img.shields.io/badge/arXiv-SpatialGen-b31b1b?logo=arxiv&logoColor=white" style="display: inline-block; vertical-align: middle;"/></a> <a href="https://github.com/manycore-research/SpatialGen" target="blank" style="margin: 2px;"><img alt="GitHub" src="https://img.shields.io/badge/GitHub-SpatialGen-24292e?logo=github&logoColor=white" style="display: inline-block; vertical-align: middle;"/></a> <a href="https://huggingface.co/manycore-research/SpatialGen-1.0" target="blank" style="margin: 2px;"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-SpatialGen-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/></a> </div>
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<p>TL;DR: Given a 3D semantic layout, SpatialGen can generate a 3D indoor scene conditioned on either a reference image (left) or a textual description (right) using a multi-view, multi-modal diffusion model.</p> </div>
✨ News
- [Sep, 2025] We released the paper of SpatialGen!
- [Aug, 2025] Initial release of SpatialGen-1.0!
📋 Release Plan
- [x] Provide inference code of SpatialGen.
- [ ] Provide training instruction for SpatialGen.
- [ ] Release SpatialGen dataset.
SpatialGen Models
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Usage
🔧 Installation
Tested with the following environment:
- Python 3.10
- PyTorch 2.3.1
- CUDA Version 12.1
# clone the repository
git clone https://github.com/manycore-research/SpatialGen.git
cd SpatialGen
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
# Optional: fix the [flux inference bug](https://github.com/vllm-project/vllm/issues/4392)
pip install nvidia-cublas-cu12==12.4.5.8📊 Dataset
We provide SpatialGen-Testset with 48 rooms, which labeled with 3D layout and 4.8K rendered images (48 x 100 views, including RGB, normal, depth maps and semantic maps) for MVD inference.
Inference
# Single image-to-3D Scene
bash scripts/infer_spatialgen_i2s.sh
# Text-to-image-to-3D Scene
# in captions/spatialgen_testset_captions.jsonl, we provide text prompts of different styles for each room,
# choose a pair of scene_id and prompt to run the text2scene experiment
bash scripts/infer_spatialgen_t2s.shLicense
SpatialGen-1.0 is derived from Stable-Diffusion-v2.1, which is licensed under the CreativeML Open RAIL++-M License.
Citation
@inproceedings{SpatialGen,
title = {SpatialGen: Layout-guided 3D Indoor Scene Generation},
author = {Fang, Chuan and Li, Heng and Liang, Yixu and Zheng, Jia and Mao, Yongsen and Liu, Yuan and Tang, Rui and Zhou, Zihan and Tan, Ping},
booktitle = {International Conference on 3D Vision},
year = {2026}
}Acknowledgements
We would like to thank the following projects that made this work possible: