GazeTrack: High-Precision Eye Tracking Based on Regularization and Spatial Computing

• URL: http://arxiv.org/abs/2511.22607v1
• Date: Thu, 27 Nov 2025 16:41:32 GMT
• Title: GazeTrack: High-Precision Eye Tracking Based on Regularization and Spatial Computing
• Authors: Xiaoyin Yang,
• Abstract summary: We design a gaze collection framework and utilize high-precision equipment to gather the first precise benchmark dataset, GazeTrack.<n>We propose a novel shape error regularization method to constrain pupil ellipse fitting and train on open-source datasets.<n>We also invent a novel coordinate transformation method similar to paper unfolding to accurately predict gaze vectors on the GazeTrack dataset.
• Score: 2.4294291235324867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Eye tracking has become increasingly important in virtual and augmented reality applications; however, the current gaze accuracy falls short of meeting the requirements for spatial computing. We designed a gaze collection framework and utilized high-precision equipment to gather the first precise benchmark dataset, GazeTrack, encompassing diverse ethnicities, ages, and visual acuity conditions for pupil localization and gaze tracking. We propose a novel shape error regularization method to constrain pupil ellipse fitting and train on open-source datasets, enhancing semantic segmentation and pupil position prediction accuracy. Additionally, we invent a novel coordinate transformation method similar to paper unfolding to accurately predict gaze vectors on the GazeTrack dataset. Finally, we built a gaze vector generation model that achieves reduced gaze angle error with lower computational complexity compared to other methods.

Related papers

• EyeTheia: A Lightweight and Accessible Eye-Tracking Toolbox [0.0]
  EyeTheia is a lightweight and open deep learning pipeline for webcam-based gaze estimation.<n>It enables real-time gaze tracking using only a standard laptop webcam.<n>It combines MediaPipe-based landmark extraction with a convolutional neural network inspired by iTracker and optional user-specific fine-tuning.
  arXiv  Detail & Related papers  (2026-01-09T19:49:01Z)
• GazeProphetV2: Head-Movement-Based Gaze Prediction Enabling Efficient Foveated Rendering on Mobile VR [0.0]
  This paper introduces a multimodal approach to VR gaze prediction that combines temporal gaze patterns, head movement data, and visual scene information.<n> Evaluations using a dataset spanning 22 VR scenes with 5.3M gaze samples show improvements in predictive accuracy when combining modalities.<n>Cross-scene generalization testing shows consistent performance with 93.1% validation accuracy and temporal consistency in predicted gaze trajectories.
  arXiv  Detail & Related papers  (2025-11-25T06:55:39Z)
• Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems [80.62854148838359]
  Eye image segmentation is a critical step in eye tracking that has great influence over the final gaze estimate. We use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data. Our methods result in robust, improved performance when tackling the discrepancy between simulation and real-world data samples.
  arXiv  Detail & Related papers  (2024-03-23T22:32:06Z)
• NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation [37.977032771941715]
  We propose a novel Head-Eye redirection parametric model based on Neural Radiance Field. Our model can decouple the face and eyes for separate neural rendering. It can achieve the purpose of separately controlling the attributes of the face, identity, illumination, and eye gaze direction.
  arXiv  Detail & Related papers  (2022-12-30T13:52:28Z)
• 3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views [67.00931529296788]
  We propose to train general gaze estimation models which can be directly employed in novel environments without adaptation. We create a large-scale dataset of diverse faces with gaze pseudo-annotations, which we extract based on the 3D geometry of the scene. We test our method in the task of gaze generalization, in which we demonstrate improvement of up to 30% compared to state-of-the-art when no ground truth data are available.
  arXiv  Detail & Related papers  (2022-12-06T14:15:17Z)
• Data-heterogeneity-aware Mixing for Decentralized Learning [63.83913592085953]
  We characterize the dependence of convergence on the relationship between the mixing weights of the graph and the data heterogeneity across nodes. We propose a metric that quantifies the ability of a graph to mix the current gradients. Motivated by our analysis, we propose an approach that periodically and efficiently optimize the metric.
  arXiv  Detail & Related papers  (2022-04-13T15:54:35Z)
• Learning-by-Novel-View-Synthesis for Full-Face Appearance-based 3D Gaze Estimation [8.929311633814411]
  This work examines a novel approach for synthesizing gaze estimation training data based on monocular 3D face reconstruction. Unlike prior works using multi-view reconstruction, photo-realistic CG models, or generative neural networks, our approach can manipulate and extend the head pose range of existing training data.
  arXiv  Detail & Related papers  (2022-01-20T00:29:45Z)
• Visual Distant Supervision for Scene Graph Generation [66.10579690929623]
  Scene graph models usually require supervised learning on large quantities of labeled data with intensive human annotation. We propose visual distant supervision, a novel paradigm of visual relation learning, which can train scene graph models without any human-labeled data. Comprehensive experimental results show that our distantly supervised model outperforms strong weakly supervised and semi-supervised baselines.
  arXiv  Detail & Related papers  (2021-03-29T06:35:24Z)
• Self-Learning Transformations for Improving Gaze and Head Redirection [49.61091281780071]
  We propose a novel generative model for images of faces, that is capable of producing high-quality images under fine-grained control over eye gaze and head orientation angles. This requires the disentangling of many appearance related factors including gaze and head orientation but also lighting, hue etc. We show that explicitly disentangling task-irrelevant factors results in more accurate modelling of gaze and head orientation.
  arXiv  Detail & Related papers  (2020-10-23T11:18:37Z)
• Towards End-to-end Video-based Eye-Tracking [50.0630362419371]
  Estimating eye-gaze from images alone is a challenging task due to un-observable person-specific factors. We propose a novel dataset and accompanying method which aims to explicitly learn these semantic and temporal relationships. We demonstrate that the fusion of information from visual stimuli as well as eye images can lead towards achieving performance similar to literature-reported figures.
  arXiv  Detail & Related papers  (2020-07-26T12:39:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.