SpatialGen: Layout-guided 3D Indoor Scene Generation

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✨ News

• [April, 2026] The official data release approval is still undergoing. Researchers interested in early access to the full dataset may apply via our Dataset Access Request Form. Please note that early access is currently limited to research use only. Requests will be reviewed and coordinated with ManyCore Inc. on a case-by-case basis.
• [Jan, 2026] The offical SpatialGen Dataset will be released soon, it is undergoing the data releasing approval process of Manycore Tech Inc.
• [Jan, 2026] We release the training code of SpatialGen, incuding the multi-view diffusion training and the gassuain optimization.
• [Nov, 2025] SpatialGen is accepted to 3DV 2026!
• [Sep, 2025] We release the paper of SpatialGen!
• [Aug, 2025] Initial release of SpatialGen-1.0!

📋 Release Plan

• Provide inference code of SpatialGen.
• Provide training instruction for SpatialGen.
• Release SpatialGen dataset.

SpatialGen Models

Usage

🔧 Installation

Tested with the following environment:

• Python 3.10
• PyTorch 2.3.1
• CUDA Version 12.1

# clone the repository
git clone --recursive  https://github.com/manycore-research/SpatialGen.git 
cd SpatialGen

python -m venv .venv
source .venv/bin/activate

pip install -r requirements.txt
pip install src/recons/Sparse-RaDeGS/submodules/diff-gaussian-rasterization
# 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

# download the pretrain weights
huggingface-cli download --resume-download manycore-research/SpatialGen-1.0 --local-dir spatialgen_ckpts

# Single image-to-3D Scene
bash scripts/infer_spatialgen_i2s.sh

# Text-to-image-to-3D Scene
# step 1. prepare controlnet conditional images, save to /path/to/your/spatialgen-testset/scene_xxxx/condition
python3 preprocess/prepare_flux_ctrlnet_conditions.py --dataset_dir /path/to/your/spatialgen-testset

# step 2. run spatialgen text2scene
# 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.sh

Training

# TODO: preprocess the dataset

# run SCM_VAE training
bash scripts/train_scm_vae.sh

# run multi-view diffusion training
bash scripts/train_spatialgen_mvd.sh

License

SpatialGen-1.0 is derived from Stable-Diffusion-v2.1, which is licensed under the CreativeML Open RAIL++-M License. FLUX.1-Wireframe-dev-lora is licensed under the FLUX.1-dev Non-Commercial 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:

DiffSplat | SD 2.1 | TAESD | FLUX | SpatialLM