Improve model card and add metadata

This PR improves the model card for Sat3DGen by adding relevant metadata and documentation.
- Added `pipeline_tag: image-to-3d` to the YAML metadata.
- Included links to the paper, GitHub repository, and project page.
- Added a sample usage code snippet from the official repository.
- Included the BibTeX citation for the paper.

README.md

@@ -1,4 +1,59 @@
----
-license: mit
----
-arxiv.org/abs/2605.14984 
+---
+license: mit
+pipeline_tag: image-to-3d
+---
+
+# Sat3DGen: Comprehensive Street-Level 3D Scene Generation from Single Satellite Image
+
+Sat3DGen is a framework for generating street-level 3D scenes from a single satellite image. It uses a geometry-first methodology to bridge the extreme viewpoint gap between satellite and street views, achieving high geometric fidelity and photorealism.
+
+[**Paper**](https://arxiv.org/abs/2605.14984) | [**Project Page**](https://qianmingduowan.github.io/Sat3DGen_project_page/) | [**GitHub**](https://github.com/qianmingduowan/Sat3DGen) | [**Demo**](https://huggingface.co/spaces/qian43/Sat3DGen)
+
+## Sample Usage
+
+To use this model, you will need the code from the [official repository](https://github.com/qianmingduowan/Sat3DGen).
+
+```python
+from source.generator import Sat3DGen
+
+# Load the model
+Sat3DGen._skip_backbone_weights = True
+model = Sat3DGen.from_pretrained("qian43/Sat3DGen")
+model = model.to("cuda:0").eval()
+
+# Proceed with inference as described in the repository
+```
+
+## Citation
+
+If you find this work useful for your research, please cite:
+
+```bibtex
+@inproceedings{
+    qian2026satdgen,
+    title={Sat3{DG}en: Comprehensive Street-Level 3D Scene Generation from Single Satellite Image},
+    author={Ming Qian and Zimin Xia and Changkun Liu and Shuailei Ma and Wen Wang and Zeran Ke and Bin Tan and Hang Zhang and Gui-Song Xia},
+    booktitle={The Fourteenth International Conference on Learning Representations},
+    year={2026},
+    url={https://openreview.net/forum?id=E7JzkZCofa}
+}
+
+@ARTICLE{Qian_2026_Sat2Densitypp,
+    author={Qian, Ming and Tan, Bin and Wang, Qiuyu and Zheng, Xianwei and Xiong, Hanjiang and Xia, Gui-Song and Shen, Yujun and Xue, Nan},
+    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
+    title={Seeing Through Satellite Images at Street Views}, 
+    year={2026},
+    volume={48},
+    number={5},
+    pages={5692-5709},
+    doi={10.1109/TPAMI.2026.3652860}}
+
+@InProceedings{Qian_2023_Sat2Density,
+    author    = {Qian, Ming and Xiong, Jincheng and Xia, Gui-Song and Xue, Nan},
+    title     = {Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs},
+    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+    month     = {October},
+    year      = {2023},
+    pages     = {3683-3692}
+}
+```