# Quantifying the Impact of Motion on 2D Gaze Estimation in Real-World Mobile Interactions

arXiv: https://arxiv.org/abs/2502.10570 · HTML: https://mlsanity.com/paper/2502.10570/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 59 references that are in the map.
- First posted on arXiv 14 Feb 2025.

## Builds on (most cited first, 19 of 19)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Gaze360: Physically Unconstrained Gaze Estimation in the Wild](https://mlsanity.com/paper/1910.10088/) (2019)
- [Eye Tracking for Everyone](https://mlsanity.com/paper/1606.05814/) (2016)
- [Appearance-Based Gaze Estimation in the Wild](https://mlsanity.com/paper/1504.02863/) (2015)
- [ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation](https://mlsanity.com/paper/2007.15837/) (2020)
- [It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation](https://mlsanity.com/paper/1611.08860/) (2016)
- [MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation](https://mlsanity.com/paper/1711.09017/) (2017)
- [Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark](https://mlsanity.com/paper/2104.12668/) (2021)
- [Few-Shot Adaptive Gaze Estimation](https://mlsanity.com/paper/1905.01941/) (2019)
- [Gaze Estimation using Transformer](https://mlsanity.com/paper/2105.14424/) (2021)
- [Automatic Gaze Analysis: A Survey of Deep Learning based Approaches](https://mlsanity.com/paper/2108.05479/) (2021)
- [Adaptive Feature Fusion Network for Gaze Tracking in Mobile Tablets](https://mlsanity.com/paper/2103.11119/) (2021)
- [Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues](https://mlsanity.com/paper/1805.03064/) (2018)
- [An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices](https://mlsanity.com/paper/2307.00122/) (2023)
- [EFE: End-to-end Frame-to-Gaze Estimation](https://mlsanity.com/paper/2305.05526/) (2023)
- [Forecasting User Attention During Everyday Mobile Interactions Using Device-Integrated and Wearable Sensors](https://mlsanity.com/paper/1801.06011/) (2018)
- [DynamicRead: Exploring Robust Gaze Interaction Methods for Reading on Handheld Mobile Devices under Dynamic Conditions](https://mlsanity.com/paper/2304.09584/) (2023)
- [SacCalib: Reducing Calibration Distortion for Stationary Eye Trackers Using Saccadic Eye Movements](https://mlsanity.com/paper/1903.04047/) (2019)
- [Accurate and Robust Eye Contact Detection During Everyday Mobile Device Interactions](https://mlsanity.com/paper/1907.11115/) (2019)

## Built on by (most cited first, 6 of 6)

- [MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Tracking](https://mlsanity.com/paper/2505.22769/) (2025)
- [TinyGaze: Lightweight Gaze-Gesture Recognition on Commodity Mobile Devices](https://mlsanity.com/paper/2604.09658/) (2026)
- [HiFiGaze: Improving Eye Tracking Accuracy Using Screen Content Knowledge](https://mlsanity.com/paper/2603.19588/) (2026)
- [A Neuro-Symbolic System for Interpretable Multimodal Physiological Signals Integration in Human Fatigue Detection](https://mlsanity.com/paper/2603.24358/) (2026)
- [Coordinate Singularities Break Conformal Coverage for Gaze and Head Pose](https://mlsanity.com/paper/2607.02565/) (2026)
- [Factor-Informed Uncertainty Distillation for Gaze Estimation](https://mlsanity.com/paper/2607.20072/) (2026)

## Abstract (arXiv)

Mobile gaze tracking on handheld devices commonly relies on appearance-based 2D gaze estimation from the device's front-facing RGB camera. Although this approach makes eye tracking more accessible by avoiding dedicated infrared hardware, maintaining accuracy remains difficult because the spatial relationship among the user's face, the camera, and the screen changes continuously during handheld use. This paper examines how motion-related geometry change affects a representative front-camera RGB 2D gaze-estimation pipeline. We report two complementary within-subject user studies ((n=10)) that capture handheld behaviour, calibration data, and gaze-estimation error during everyday-style mobile tasks across conditions ranging from lying and sitting to walking and maze navigation. Rather than treating motion as a single binary factor, we analyse within-task versus task-switch behaviour, calibration-test mismatch, and condition-specific motion signatures. Behavioural results show that head-to-camera distance is comparatively stable during sustained within-task use, while task switches introduce much larger geometry fluctuations, with head-distance standard deviations of approximately 7.42--10.10,cm across conditions. In the gaze-evaluation study, one-off calibration reduced average RMSE from 4.49,cm to 3.44,cm; under this regime, the global effect of motion condition on RMSE was not statistically reliable, whereas clearer patterns appeared in task-specific and motion-specific calibration analyses, where better calibration-test match reduced error.