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Citation
Hayeon Kim and In-Kwon Lee, âReAlign: Closed-Loop Support for Continuous Virtual Reality Tasks,â IEEE Transactions on Visualization and Computer Graphics (TVCG), accepted, also will be presented in IEEE ISMAR 2026.
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Abstract
Continuous virtual reality (VR) tasks require sustained interaction, but human task behavior naturally fluctuates. We frame support in these tasks as a closed-loop policy problem: passive head-mounted display signals provide an online, operational estimate of task-directed stability (how consistently ongoing behavior remains aligned with the task), and declines in this estimate trigger brief, response-free cues. Because such a policy cannot be judged by average performance alone, we propose a three-axis evaluation framework covering event-locked recovery, efficacy maintenance across repeated exposures, and bounded false-alarm cost. We instantiate this framing in the ReAlign pipeline, an engine-aware system driven by gaze dispersion and head-motion jitter, which governs a state-triggered Adaptive policy. In a within-subjects study (), the Adaptive policy was associated with faster event-locked recovery than a perceptually matched fixed-interval Periodic policy (median Time-to-Realignment 6.2 vs.10.8s), flatter efficacy decay across repeated cue deliveries, and lower perceived disruption; catch trials delivered during high-stability periods incurred a reaction-time delay within a prespecified ms equivalence margin. These findings suggest that closed-loop support can serve as lightweight task scaffolding. Beyond this system, the framework offers reusable policy-level evaluation logic for adaptive extended reality (XR) support.
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