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Depth Perception and Spatial Vision01:15

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Controlled Rotation of Human Observers in a Virtual Reality Environment
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Head-EyeK: Head-Eye Coordination and Control Learned in Virtual Reality.

Yifang Pan, Ludwig Sidenmark, Karan Singh

    IEEE Transactions on Visualization and Computer Graphics
    |July 15, 2025
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    This study introduces Head-EyeK, a novel model for realistic human head-eye coordination in virtual reality. It accurately animates complex gaze behaviors by integrating physiological and psychological factors.

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    Area of Science:

    • Human-Computer Interaction
    • Computer Graphics
    • Psychology
    • Robotics

    Background:

    • Human head-eye coordination is complex, influenced by physiology, psychology, and intent.
    • Existing gaze models in psychology and graphics lack generalizability for realistic head-eye coordination.

    Purpose of the Study:

    • To develop and validate a novel head-eye coordinated Inverse Kinematic gaze model (Head-EyeK).
    • To identify key factors influencing human head-eye coordination.
    • To animate complex gaze behaviors credibly in virtual reality.

    Main Methods:

    • Developed an experimental protocol to collect head-eye motion data in Virtual Reality (VR) during sequential look-at tasks.
    • Utilized collected data to identify factors influencing head-eye coordination.
    • Introduced the Head-EyeK model, integrating identified insights into an Inverse Kinematic framework.

    Main Results:

    • Evaluated Head-EyeK's algorithmic parameters' impact on gaze behavior.
    • Demonstrated quantitative superiority over prior art using ground-truth data.
    • Showcased qualitative improvements via a perceptual study and credible animation of complex gaze behaviors.

    Conclusions:

    • Head-EyeK provides a significant advancement in modeling human head-eye coordination.
    • The model credibly animates complex gaze behaviors, outperforming existing methods.
    • This research contributes to more realistic human-like interactions in virtual environments.