🤖 AI Summary
This study addresses the limitations of existing extended reality (XR) tools in integrating XR stimuli as reusable components throughout the visualization research lifecycle. We propose reVISit-XR, a novel framework that treats WebXR stimuli as first-class research components. By providing reusable building blocks and research integration packages, combined with semantic state capture, session trajectory tracking, and data reinjection mechanisms, the system enables the embedding, tracking, and replay of XR stimuli. Validated through seven integration examples and a deployment study, reVISit-XR achieves seamless integration between XR environments and standard research workflows, significantly enhancing both the efficiency and analytical depth of empirical studies.
📝 Abstract
Extended reality (XR) is increasingly a setting for empirical visualization research, where study-relevant state is distributed across headset and controller pose, scene configuration, selections, spatial layouts, and AR anchors. Existing XR tools support parts of the workflow, such as scene authoring, interaction, or session analysis, yet seldom treat an XR stimulus as a reusable component of a complete study lifecycle. We present reVISit-XR, an extension of reVISit that makes customizable WebXR stimuli embeddable, trackable, and replayable within empirical visualization studies. reVISit-XR sequences XR scenes alongside standard study components, collects reactive task responses, captures scene-authored semantic state together with generic XR traces, and rehydrates participant sessions for later desktop and headset analysis. Organized as a reusable stimulus build package and a study integration package, it lets XR stimuli act as first-class study components. We demonstrate its scope and feasibility through seven integrated, reusable examples and a deployed study, and discuss its current capabilities and future extensions.