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+# HaHeAE: Learning Generalisable Joint Representations of Human Hand and Head Movements in Extended Reality
+
+
+## Abstract
+```
+Human hand and head movements are the most pervasive input modalities in extended reality (XR) and are significant for a wide range of applications. 
+However, prior works on hand and head modelling in XR only explored a single modality or focused on specific applications. 
+We present HaHeAE - a novel self-supervised method for learning generalisable joint representations of hand and head movements in XR. 
+At the core of our method is an autoencoder (AE) that uses a graph convolutional network-based semantic encoder and a diffusion-based stochastic encoder to learn the joint semantic and stochastic representations of hand-head movements. 
+It also features a diffusion-based decoder to reconstruct the original signals. 
+Through extensive evaluations on three public XR datasets, we show that our method 1) significantly outperforms commonly used self-supervised methods by up to 74.1% in terms of reconstruction quality and is generalisable across users, activities, and XR environments, 2) enables new applications, including interpretable hand-head cluster identification and variable hand-head movement generation, and 3) can serve as an effective feature extractor for downstream tasks. 
+Together, these results demonstrate the effectiveness of our method and underline the potential of self-supervised methods for jointly modelling hand-head behaviours in extended reality.
+```
+
+
+## Environment:
+Ubuntu 22.04
+python 3.8+
+pytorch 1.8.1
+
+
+## Usage:
+Step 1: Create the environment
+```
+conda env create -f ./environment/haheae.yaml -n haheae
+conda activate haheae
+```
+
+Step 2: Follow the instructions at the [Pose2Gaze project][1] to process the datasets.
+
+
+Step 3:  Set 'data_dir' in 'config.py' and 'main.py' for the processed datasets. Run 'train.sh' to evaluate the pre-trained models. If you want to train the model from scratch, you can remove the pre-trained models and uncomment the training command (the command with "mode" set to "train").
+
+ 
+## Citation
+
+```bibtex
+@inproceedings{hu25hoigaze,
+	title={HOIGaze: Gaze Estimation During Hand-Object Interactions in Extended Reality Exploiting Eye-Hand-Head Coordination},
+	author={Hu, Zhiming and Haeufle, Daniel and Schmitt, Syn and Bulling, Andreas},
+	booktitle={Proceedings of the 2025 ACM Special Interest Group on Computer Graphics and Interactive Techniques},
+	year={2025}}
+	
+@article{hu24pose2gaze,
+	author={Hu, Zhiming and Xu, Jiahui and Schmitt, Syn and Bulling, Andreas},
+	journal={IEEE Transactions on Visualization and Computer Graphics}, 
+	title={Pose2Gaze: Eye-body Coordination during Daily Activities for Gaze Prediction from Full-body Poses}, 
+	year={2024}}
+```
+
+
+## Acknowledgements
+Our work is built on the codebase of [Diffusion Autoencoders][2] and [DisMouse][3]. Thanks to the authors for sharing their codes.
+
+[1]: https://github.com/CraneHzm/Pose2Gaze
+[2]: https://diff-ae.github.io/
+[3]: https://git.hcics.simtech.uni-stuttgart.de/public-projects/DisMouse