first commit

README.md

@@ -0,0 +1,46 @@
+# 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
+
+```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}}
+```