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--- |
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license: apache-2.0 |
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pipeline_tag: image-to-3d |
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--- |
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# TriplaneGuassian Model Card |
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<div align="center"> |
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[**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) |
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</div> |
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## Introduction |
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TGS enables fast reconstruction from single-view image in a few seconds based on a hybrid Triplane-Gaussian 3D representation. |
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## Examples |
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### Results on Images Generated by [Midjourney](https://www.midjourney.com/) |
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<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/644dbf6453ad80c6593bf748/BcJp8alZRXAIdPmfbVGdx.qt"></video> |
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### Results on Captured Real-world Images |
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<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/644dbf6453ad80c6593bf748/bgAxqUQpnisQAmsGZ9Q_0.qt"></video> |
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## Model Details |
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The model `model_lvis_rel.ckpt` is trained on Objaverse-LVIS dataset, which only includes ~45K synthetic objects. |
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## Usage |
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You can directly download the model in this repository or employ the model in python script by: |
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```python |
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from huggingface_hub import hf_hub_download |
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MODEL_CKPT_PATH = hf_hub_download(repo_id="VAST-AI/TriplaneGaussian", filename="model_lvis_rel.ckpt", repo_type="model") |
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``` |
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More details can be found in our [Github repository](https://github.com/VAST-AI-Research/TriplaneGaussian). |
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## Citation |
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If you find this work helpful, please consider citing our paper: |
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```bibtex |
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@article{zou2023triplane, |
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title={Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers}, |
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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}, |
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journal={arXiv preprint arXiv:2312.09147}, |
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year={2023} |
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} |
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``` |
