HybridGaze: A Naturalistic Dataset and Multistream Model for Robust Gaze Estimation
Abstract
Gaze estimation plays a central role in computer vision and human-computer interaction, enabling applications in assistive systems, attention modeling, and human-robot collaboration. However, existing datasets often rely on infrared-based hardware, are collected in constrained laboratory environments, or lack precise synchronization between stimuli and gaze data, which limits model generalization to real-world conditions.To address these challenges, we present HybridGaze - an open-source eye tracking dataset collected using a Tobii tracker combined with a standard RGB webcam. The recordings are processed into eye images and facial landmarks, providing synchronized gaze annotations and facial information across a variety of visual tasks. By capturing gaze data in naturalistic settings, the dataset reflects real-world visual behavior and serves as a valuable benchmark for gaze estimation research.
Furthermore, we introduce GazeModalNet, a multi-stream neural network that estimates gaze direction from two complementary sources: eye images and facial landmarks. Together, the dataset and model establish a strong foundation for developing robust, multimodal gaze estimation systems beyond laboratory constraints.
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