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  - pytorch_model_hub_mixin
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- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
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- - Library: [More Information Needed]
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- - Docs: [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - pytorch_model_hub_mixin
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  ---
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+ <div align="center">
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+ <h1>VGGT: Visual Geometry Grounded Transformer</h1>
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+ <a href="https://jytime.github.io/data/VGGT_CVPR25.pdf" target="_blank" rel="noopener noreferrer">
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+ <img src="https://img.shields.io/badge/Paper-VGGT" alt="Paper PDF">
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+ </a>
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+ <a href="https://arxiv.org/abs/2503.11651"><img src="https://img.shields.io/badge/arXiv-2503.11651-b31b1b" alt="arXiv"></a>
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+ <a href="https://vgg-t.github.io/"><img src="https://img.shields.io/badge/Project_Page-green" alt="Project Page"></a>
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+ <a href='https://huggingface.co/spaces/facebook/vggt'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue'></a>
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+ **[Meta AI Research](https://ai.facebook.com/research/)**; **[University of Oxford, VGG](https://www.robots.ox.ac.uk/~vgg/)**
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+ [Jianyuan Wang](https://jytime.github.io/), [Minghao Chen](https://silent-chen.github.io/), [Nikita Karaev](https://nikitakaraevv.github.io/),
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+ [Andrea Vedaldi](https://www.robots.ox.ac.uk/~vedaldi/), [Christian Rupprecht](https://chrirupp.github.io/), [David Novotny](https://d-novotny.github.io/)
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+ </div>
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+ ## Overview
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+ Visual Geometry Grounded Transformer (VGGT, CVPR 2025) is a feed-forward neural network that directly infers all key 3D attributes of a scene, including extrinsic and intrinsic camera parameters, point maps, depth maps, and 3D point tracks, **from one, a few, or hundreds of its views, within seconds**.
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+ ## Quick Start
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+ Please refer to our [Github Repo](https://github.com/facebookresearch/vggt)
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+ ## Citation
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+ If you find our repository useful, please consider giving it a star ⭐ and citing our paper in your work:
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+ ```bibtex
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+ @inproceedings{wang2025vggt,
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+ title={VGGT: Visual Geometry Grounded Transformer},
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+ author={Wang, Jianyuan and Chen, Minghao and Karaev, Nikita and Vedaldi, Andrea and Rupprecht, Christian and Novotny, David},
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+ booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
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+ year={2025}
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+ }
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+ ```