---
title: Extend3D
emoji: 🧩
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.34.2
python_version: "3.10"
app_file: app.py
suggested_hardware: a10g-large
tags:
- 3d-generation
- image-to-3d
- gaussian-splatting
- mesh
short_description: "[CVPR 2026] Extend3D: Town-Scale 3D Generation"
pinned: false
---

## 🛠Preparation
### Environment
- Linux x86-64 system
- NVIDIA GPU with 24GB VRAM ($a=b=2$)
- CUDA version ≥ 12.4
Larger scene generation may require more VRAM.
### Install
```bash
conda create -n extend3d python=3.10
conda activate extend3d
pip install -r requirements.txt
```
If your GPU does not support pytorch-2.4.0, follow instructions in [SETUP.md](./SETUP.md).
## 🚀 Usage
### Quick Start
```python
from extend3d import Extend3D
from PIL import Image
import imageio
from trellis.utils import render_utils, postprocessing_utils
pipeline = Extend3D.from_pretrained("microsoft/TRELLIS-image-large").cuda()
image = Image.open("assets/examples/0.png")
output = pipeline.run(image)
video = render_utils.render_video(output['gaussian'][0], r=1.6, resolution=1024)['color']
imageio.mimsave('sample_gs.mp4', video, fps=30)
glb = postprocessing_utils.to_glb(
output['gaussian'][0],
output['mesh'][0],
simplify=0.9,
texture_size=1024
)
glb.export(os.path.join(args.output_dir, 'sample.glb'))
```
You may follow [example.py](./example.py) for detailed hyper-parameters.
### Gradio Demo
```bash
python app.py
```
## 📚 Citation
```bibtex
@inproceedings{yoon2026extend3d,
title = {Extend3D: Town-scale 3D Generation},
author = {Yoon, Seungwoo, and Kim, Jinmo, and Park, Jaesik},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference},
year = {2026}
}
```
## Acknowledgement
This repository is based on the implementation from [Trellis](https://github.com/microsoft/TRELLIS/tree/442aa1e1afb9014e80681d3bf604e8d728a86ee7).
We sincerely thank the authors for releasing their code.
We also thank the anonymous reviewers for their insightful and constructive feedback.