| --- |
| language: |
| - en |
| license: mit |
| metrics: |
| - recall |
| - precision |
| - f1 |
| pipeline_tag: image-to-3d |
| tags: |
| - 3d-human-reconstruction |
| - human-scene-contact |
| - monocular-rgb |
| - 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. |
