Add model card, pipeline tag, and links to paper and code

README.md

@@ -1,3 +1,46 @@
 ---
 license: mit
+pipeline_tag: image-to-3d
 ---
+
+# StructSplat: Generalizable 3D Gaussian Splatting from Uncalibrated Sparse Views
+
+This repository contains the pretrained weights for **StructSplat**, a feed-forward and generalizable 3D Gaussian reconstruction framework that operates directly on uncalibrated images without requiring camera parameters.
+
+* **Paper:** [StructSplat: Generalizable 3D Gaussian Splatting from Uncalibrated Sparse Views](https://huggingface.co/papers/2606.28321)
+* **Project Page:** [https://structsplat.github.io](https://structsplat.github.io)
+* **Code:** [https://github.com/J-C-Zhao/StructSplat](https://github.com/J-C-Zhao/StructSplat)
+
+## Installation & Evaluation
+
+To set up the environment and run training or evaluation, please refer to the instructions in the [GitHub Repository](https://github.com/J-C-Zhao/StructSplat).
+
+### Setup Environment
+
+```bash
+conda create -n structsplat python=3.10.19
+conda activate structsplat
+pip install torch==2.4.0 torchvision==0.19.0 -i https://download.pytorch.org/whl/cu118
+pip install -r requirements.txt
+```
+
+### Evaluation
+
+Run the following command to evaluate the model:
+
+```bash
+python evaluation.py -c config/dl3dv.yaml
+```
+
+## Citation
+
+If you find this work useful, please cite the paper:
+
+```bibtex
+@inproceedings{zhao2026structsplat,
+  title={StructSplat: Generalizable 3D Gaussian Splatting from Uncalibrated Sparse Views},
+  author={Zhao, Jia-Chen and Chen, Beiqi and Chen, Xinyang and Wang, Guangcong and Nie, Liqing},
+  booktitle={European Conference on Computer Vision},
+  year={2026}
+}
+```