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README.md

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+---
+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}
+}
+```