A Deep Learning-Based Study on Gaze Prediction in Virtual Environments
Feng Luo · Advances in transdisciplinary engineering · 2025
With the rapid advancement of Virtual Reality (VR) and Augmented Reality (AR) technologies, accurately predicting users’ gaze points in virtual environments has become crucial for improving both design quality and user interactivity. This study investigates the effectiveness of three machine learning algorithms—Random Forest, Logistic Regression, and XGBoost—using the FixationNet dataset for gaze prediction tasks. Through comprehensive experiments, Random Forest consistently demonstrates superior performance across multiple evaluation metrics, including accuracy, precision, recall, and F1-score. XGBoost follows closely behind, while Logistic Regression exhibits comparatively weaker results. Furthermore, multidimensional visualization analyses are employed to reveal deeper insights into model behavior, confirming the robustness and adaptability of Random Forest in capturing complex gaze patterns within dynamic virtual spaces. These findings provide a valuable reference for future research on user behavior modeling and immersive environment optimization.