TripoSplat converts a single 2D image into high-quality and variable number of 3D Gaussians, developed by TripoAI. It can serve as a powerful pipeline tool for asset creation, AR/VR, game development, simulation environments, and beyond. This is the inference-only repo for TripoSplat. For the training code, see TripoSplat-Training.

• High-quality, versatile generation that handles a wide range of image styles.
• Arbitrary Gaussian count (up to 262,144) — trade off visual quality against rendering cost according to your need.
• Minimal, readable code: two files (triposplat.py and model.py), ~2,000 LOC total. Easy to customize and integrate into other ecosystems.
• Near-zero dependencies: no transformers, no diffusers, no version-conflict hell. Runs on any platform.
• Official ComfyUI support: drop the official workflow template into ComfyUI and start playing with TripoSplat right away.

Download model weights to ckpts/ from HuggingFace.

# Use one of the following ways to download model weights.

# 1. Use HuggingFace CLI
hf download VAST-AI/TripoSplat --local-dir ckpts/

# 2. Use huggingface_hub
pip install huggingface_hub
python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='VAST-AI/TripoSplat', local_dir='ckpts/')"

# 3. Use ModelScope CLI
pip install modelscope
modelscope download VAST-AI-Research/TripoSplat --local_dir ckpts/

# 4. Use modelscope Python SDK
pip install modelscope
python -c "from modelscope import snapshot_download; snapshot_download('VAST-AI-Research/TripoSplat', local_dir='ckpts/')"

# 5. Manual download from HuggingFace / ModelScope.

Setup the environment and run the example inference script.

# install torch and torchvision according to your environment
pip install numpy safetensors pillow tqdm
python run_example.py

The exported .ply / .splat files can be visualized in any 3D Gaussian viewer — e.g. SparkJS or SuperSplat.

pip install gradio
python run_gradio.py

TripoSplat code and weight models are released under the MIT License.

If you find TripoSplat useful, please cite:

@misc{yan2026generative3dgaussianslearned,
    title={Generative 3D Gaussians with Learned Density Control}, 
    author={Runjie Yan and Yan-Pei Cao and Peng Wang and Ding Liang and Yuan-Chen Guo},
    year={2026},
    eprint={2605.16355},
    archivePrefix={arXiv},
    primaryClass={cs.GR},
    url={https://arxiv.org/abs/2605.16355}, 
}