Task Breakpoint Generation using Origin-Centric Graph in Virtual Reality Recordings for Adaptive Playback

TL;DR

This paper introduces an Origin-Centric Graph-based method for automatically segmenting goal-oriented VR activity recordings into task units, enabling adaptive playback and improving upon manual or 2D video-based segmentation approaches.

Contribution

It presents a novel automatic task segmentation technique using OCG and STSG for VR recordings, addressing limitations of existing manual and 2D methods.

Findings

High agreement between algorithm and user-perceived breakpoints

Accurate detection of task boundaries in VR scenarios

Supports adaptive VR playback based on task segmentation

Abstract

We propose a method for generating task breakpoints based on an Origin-Centric Graph (OCG) to segment goal-oriented activity recordings into task units for adaptive playback in Virtual Reality (VR) environments. With the development of Augmented Reality (AR)/VR head-mounted displays (HMDs), research on adaptive tutorials and authoring tools has become active, but existing task segmentation methods mainly rely on manual annotation or are restricted to 2D video which limits their applicability to 3D VR contexts. In our approach, assembly scenarios with clearly defined task boundaries are recorded using a structured spatio-temporal scene graph (STSG), and the OCG is employed to track changes in the central object and the formation of new groups, thereby generating task breakpoints automatically. A user study collected user-perceived task breakpoints to establish ground truth (GT), and comparison with the algorithm-detected breakpoints demonstrated high agreement and confirmed accuracy in supporting adaptive playback. The proposed task segmentation method provides a foundation for dynamically adjusting VR playback according to user proficiency and progress, with potential for extension into automatic timeline segmentation systems for diverse VR recordings.