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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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+
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+ # TriplaneGuassian Model Card
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+
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+ <div align="center">
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+
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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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+
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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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+
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+ ## Examples
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+
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+ ### Results on Images Generated by [Midjourney](https://www.midjourney.com/)
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+
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+ <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/644dbf6453ad80c6593bf748/BcJp8alZRXAIdPmfbVGdx.qt"></video>
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+
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+ ### Results on Captured Real-world Images
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+
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+ <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/644dbf6453ad80c6593bf748/bgAxqUQpnisQAmsGZ9Q_0.qt"></video>
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+
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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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+
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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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+
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+ More details can be found in our [Github repository](https://github.com/VAST-AI-Research/TriplaneGaussian).
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+
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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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+ ```
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