Improve model card

Hi! I'm Niels from the Hugging Face community science team. I noticed this repository didn't have a full model card yet, so I've opened this PR to add a description of the project, including its key features and links to the paper and the official GitHub repository. Providing this information helps researchers and developers understand the model's capabilities and use cases more easily.

README.md

@@ -1,7 +1,7 @@
 ---
-license: mit
 language:
 - en
+license: mit
 metrics:
 - recall
 - precision
@@ -14,4 +14,45 @@ tags:
 - mesh-reconstruction
 - pose-aware
 - icme-2026
----
+---
+
+# GraphiContact: Pose-aware Human-Scene Robust Contact Perception for Interactive Systems
+
+This repository contains the pre-trained checkpoints for **GraphiContact**, a novel framework for monocular vertex-level human-scene contact prediction and 3D human mesh reconstruction.
+
+[**Paper (arXiv)**](https://huggingface.co/papers/2603.20310) | [**Official GitHub Repository**](https://github.com/Aveiro-Lin/GraphiContact)
+
+## Overview
+
+GraphiContact jointly addresses vertex-level contact prediction and single-image 3D human mesh reconstruction. It uses reconstructed body geometry as a scaffold for contact reasoning, integrating pose-aware features with human-scene interaction understanding.
+
+### Key Features
+
+*   **Pose-aware Framework**: Transfers complementary human priors from pretrained Transformer encoders to predict per-vertex human-scene contact on the reconstructed mesh.
+*   **SIMU Training Strategy**: Introduces a Single-Image Multi-Infer Uncertainty (SIMU) training strategy with token-level adaptive routing. This simulates occlusion and noisy observations during training while preserving efficient single-branch inference at test time.
+*   **Robust Perception**: Specifically designed to handle real-world scenarios with perceptual noise and occlusions, making it suitable for interactive systems like embodied AI and rehabilitation analysis.
+
+<p align="center">
+  <img src="https://github.com/Aveiro-Lin/GraphiContact/raw/main/docs/Overview.png" width="850">
+</p>
+
+## Installation and Usage
+
+For detailed instructions on environment setup, downloading model weights, and running inference demos, please refer to the [official GitHub repository](https://github.com/Aveiro-Lin/GraphiContact).
+
+## Citation
+
+If you find this work useful for your research, please consider citing the paper:
+
+```bibtex
+@inproceedings{lin2026graphicontact,
+  title={GraphiContact: Pose-aware Human-Scene Robust Contact Perception for Interactive Systems},
+  author={Lin, Aveiro and others},
+  booktitle={IEEE International Conference on Multimedia and Expo (ICME)},
+  year={2026}
+}
+```
+
+## License
+
+The research code is released under the **MIT license**. Note that the model has dependencies on the SMPL and MANO models, which are subject to their own [Software Copyright License](https://smpl.is.tue.mpg.de/modellicense) for non-commercial scientific research purposes.