README.md

---
license: apache-2.0
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
library_name: transformers
base_model:
- Qwen/Qwen2-VL-2B
---

# G2VLM-2B-MoT
## Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning

<p align="left">
  <img src="https://huggingface.co/InternRobotics/G2VLM-2B-MoT/resolve/main/assets/icon.png" alt="G2VLM" width="200"/>
</p>


<p align="left">
  <a href="https://gordonhu608.github.io/g2vlm.github.io/">
    <img
      src="https://img.shields.io/badge/G2VLM-Website-0A66C2?logo=safari&logoColor=white" style="display: inline-block; vertical-align: middle;"
      alt="G2VLM Website"
    />
  </a>
  <a href="https://arxiv.org/abs/2511.21688">
    <img
      src="https://img.shields.io/badge/G2VLM-Paper-red?logo=arxiv&logoColor=red" style="display: inline-block; vertical-align: middle;"
      alt="G2VLM Paper on arXiv"
    />
  </a>
  <a href="https://github.com/InternRobotics/G2VLM" target="_blank" style="margin: 2px;">
      <img 
        alt="Github" src="https://img.shields.io/badge/G2VLM-Codebase-536af5?color=536af5&logo=github" style="display: inline-block; vertical-align: middle;"
        alt="G2VLM Codebase"
      />
  </a>
</p>


> We present <b>G<sup>2</sup>VLM</b>, a geometry grounded vision-language model proficient in both spatial 3D reconstruction and spatial understanding tasks. For spatial reasoning questions, G<sup>2</sup>VLM can natively predict 3D geometry and employ interleaved reasoning for an answer.


This repository hosts the model weights for <b>G<sup>2</sup>VLM</b>. For installation, usage instructions, and further documentation, please visit our [GitHub repository](https://github.com/InternRobotics/G2VLM).


<p align="left"><img src="https://huggingface.co/InternRobotics/G2VLM-2B-MoT/resolve/main/assets/teaser.png" width="100%"></p>



## 🧠 Method
G<sup>2</sup>VLM is a unified model that integrates both a geometric perception expert for 3D reconstruction and a semantic perception expert for multimodal understanding and spatial reasoning tasks. All tokens can do shared multi-modal self attention in each transformer block.

<p align="left"><img src="https://huggingface.co/InternRobotics/G2VLM-2B-MoT/resolve/main/assets/method.png" width="100%"></p>


## License
G2VLM is licensed under the Apache 2.0 license.

## ✍️ Citation
```bibtex
@article{hu2025g2vlmgeometrygroundedvision,
      title={G$^2$VLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning}, 
      author={Wenbo Hu and Jingli Lin and Yilin Long and Yunlong Ran and Lihan Jiang and Yifan Wang and Chenming Zhu and Runsen Xu and Tai Wang and Jiangmiao Pang},
      year={2025},
      eprint={2511.21688},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2511.21688}, 
}
```