FocusFlow: 3D Gaze-Depth Interaction in Virtual Reality Leveraging Active Visual Depth Manipulation

TL;DR

FocusFlow introduces a novel gaze-depth interaction method in VR that leverages visual depth perception, a binocular detection algorithm, and adaptive learning strategies to enhance user experience and control accuracy.

Contribution

This paper presents the first binocular visual depth detection algorithm and a 'Virtual Window' interface for gaze-depth interaction in VR, along with adaptive learning strategies for users.

Findings

User studies with 24 participants show high usability of the virtual window interface.

Effective learning strategies improve users' mastery of gaze-depth control.

Gaze-depth interaction enhances VR experience by utilizing visual depth cues.

Abstract

Gaze interaction presents a promising avenue in Virtual Reality (VR) due to its intuitive and efficient user experience. Yet, the depth control inherent in our visual system remains underutilized in current methods. In this study, we introduce FocusFlow, a hands-free interaction method that capitalizes on human visual depth perception within the 3D scenes of Virtual Reality. We first develop a binocular visual depth detection algorithm to understand eye input characteristics. We then propose a layer-based user interface and introduce the concept of 'Virtual Window' that offers an intuitive and robust gaze-depth VR interaction, despite the constraints of visual depth accuracy and precision spatially at further distances. Finally, to help novice users actively manipulate their visual depth, we propose two learning strategies that use different visual cues to help users master visual depth control. Our user studies on 24 participants demonstrate the usability of our proposed virtual window concept as a gaze-depth interaction method. In addition, our findings reveal that the user experience can be enhanced through an effective learning process with adaptive visual cues, helping users to develop muscle memory for this brand-new input mechanism. We conclude the paper by discussing strategies to optimize learning and potential research topics of gaze-depth interaction.