Comparing Gaze, Head and Controller Selection of Dynamically Revealed Targets in Head-Mounted Displays

Area of Science:

• Human-Computer Interaction
• Virtual Reality
• Usability Engineering

Background:

• Existing head-mounted virtual reality (VR) studies often assume targets are always visible.
• Dynamic target revelation in VR presents unique interaction challenges.
• Understanding input modality performance is crucial for immersive VR applications.

Purpose of the Study:

• To compare pointing and selection performance using gaze, head, and controller input in VR.
• To investigate the impact of field of view, target characteristics, and location knowledge on input modality performance.
• To evaluate the applicability of existing pointing models to different input methods in VR.

Main Methods:

• A head-mounted virtual reality study was conducted.
• Participants performed selection tasks with dynamically revealed targets using gaze, head, and controller pointing.
• Factors manipulated included screen width (field of view), target amplitude and width, and prior knowledge of target location.

Main Results:

• Gaze and controller pointing were significantly faster than head pointing.
• Increased screen width improved performance up to a point.
• Existing two-component pointing models were suitable for all modalities, with specific differences noted for gaze performance with known versus unknown target locations.

Conclusions:

• Gaze and controller pointing offer superior performance over head pointing for dynamic target selection in VR.
• Field of view, target properties, and location predictability significantly influence VR interaction efficiency.
• The findings provide empirical data to inform the design of more effective VR input systems, especially for applications with dynamic or peripheral interactions.