HOIGaze: Gaze Estimation During Hand-Object Interactions in Extended Reality Exploiting Eye-Hand-Head Coordination

Project homepage: https://zhiminghu.net/hu25_hoigaze.

Abstract

We present HOIGaze – a novel learning-based approach for gaze estimation during hand-object interactions (HOI) in extended reality (XR). 
HOIGaze addresses the challenging HOI setting by building on one key insight: The eye, hand, and head movements are closely coordinated during HOIs and this coordination can be exploited to identify samples that are most useful for gaze estimator training – as such, effectively denoising the training data.
This denoising approach is in stark contrast to previous gaze estimation methods that treated all training samples as equal. 
Specifically, we propose: 1) a novel hierarchical framework that first recognises the hand currently visually attended to and then estimates gaze direction based on the attended hand; 2) a new gaze estimator that uses cross-modal Transformers to fuse head and hand-object features extracted using a convolutional neural network and a spatio-temporal graph convolutional network; and 3) a novel eye-head coordination loss that upgrades training samples belonging to the coordinated eye-head movements. 
We evaluate HOIGaze on the HOT3D and Aria digital twin (ADT) datasets and show that it significantly outperforms state-of-the-art methods, achieving an average improvement of 15.6% on HOT3D and 6.0% on ADT in mean angular error. 
To demonstrate the potential of our method, we further report significant performance improvements for the sample downstream task of eye-based activity recognition on ADT. 
Taken together, our results underline the significant information content available in eye-hand-head coordination and, as such, open up an exciting new direction for learning-based gaze estimation.

Environment:

Ubuntu 22.04 python 3.8+ pytorch 1.8.1

Usage:

Step 1: Create the environment

conda env create -f ./environment/hoigaze.yaml -n hoigaze
conda activate hoigaze

Step 2: Follow the instructions in './adt_processing/' and './hot3d_processing/' to process the datasets.

Step 3: Set 'data_dir' and 'cuda_idx' in 'train_hot3d_userX.sh' (X for 1, 2, or 3) to evaluate on HOT3D for different users. Set 'data_dir' and 'cuda_idx' in 'train_hot3d_sceneX.sh' (X for 1, 2, or 3) to evaluate on HOT3D for different scenes.

Step 4: Set 'data_dir' and 'cuda_idx' in 'train_adt.sh' to evaluate on ADT.

Citation

@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}}