README.md

---
license: apache-2.0
pipeline_tag: image-to-3d
---

# TriplaneGuassian Model Card

<div align="center">

[**Project Page**](https://zouzx.github.io/TriplaneGaussian/) **|** [**Paper (ArXiv)**](https://arxiv.org/abs/2312.09147) **|** [**Code**](https://github.com/VAST-AI-Research/TriplaneGaussian) **|** [**Gradio demo**](https://huggingface.co/spaces/VAST-AI/TriplaneGaussian)
</div>

## Introduction
TGS enables fast reconstruction from single-view image in a few seconds based on a hybrid Triplane-Gaussian 3D representation.

## Examples

### Results on Images Generated by [Midjourney](https://www.midjourney.com/)

<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/644dbf6453ad80c6593bf748/BcJp8alZRXAIdPmfbVGdx.qt"></video>

### Results on Captured Real-world Images

<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/644dbf6453ad80c6593bf748/bgAxqUQpnisQAmsGZ9Q_0.qt"></video>

## Model Details
The model `model_lvis_rel.ckpt` is trained on Objaverse-LVIS dataset, which only includes ~45K synthetic objects. 

## Usage
You can directly download the model in this repository or employ the model in python script by:
```python
from huggingface_hub import hf_hub_download
MODEL_CKPT_PATH = hf_hub_download(repo_id="VAST-AI/TriplaneGaussian", filename="model_lvis_rel.ckpt", repo_type="model")
```

More details can be found in our [Github repository](https://github.com/VAST-AI-Research/TriplaneGaussian).

## Citation
If you find this work helpful, please consider citing our paper:
```bibtex
@article{zou2023triplane,
  title={Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers},
  author={Zou, Zi-Xin and Yu, Zhipeng and Guo, Yuan-Chen and Li, Yangguang and Liang, Ding and Cao, Yan-Pei and Zhang, Song-Hai},
  journal={arXiv preprint arXiv:2312.09147},
  year={2023}
}
```