Conflict Resolution Strategies for Co-manipulation of Virtual Objects Under Non-disjoint Conditions

2026-03-29

AI Summary

This study addresses operational conflicts in virtual reality (VR) collaborative editing of shared objects caused by non-mutually-exclusive subcomponent selection among multiple users. The authors propose the first systematic solution that integrates preventive strategies—encompassing object-level and action-level constraints—with reactive mechanisms such as vertex selection averaging and subordinate-user prioritization to computationally resolve conflicts. In a VR wireframe editing experiment involving 76 participants, action-level constraints significantly outperformed conventional object locking; the averaging approach achieved the best trade-off between efficiency and user experience; and the effectiveness of each strategy was found to be highly dependent on task type, user expertise, and collaboration mode. This work provides the first comprehensive treatment of collaborative conflicts under non-exclusive selection, establishing both theoretical and practical foundations for flexible and efficient VR collaborative editing.

Abstract

Virtual Reality (VR) co-manipulation enables multiple users to collaboratively interact with shared virtual objects. However, existing research treats objects as monolithic entities, overlooking scenarios where users need to manipulate different sub-components simultaneously. This work addresses conflict resolution when users select overlapping vertices (non-disjoint sets) during co-manipulation. We present a comprehensive framework comprising preventive strategies (Object-level and Action-level Restrictions) and reactive strategies (computational conflict resolution). Through two user studies with 76 participants (38 pairs), we evaluated these approaches in collaborative wireframe editing tasks. Study 1 identified Averaging as the optimal computational method, balancing task efficiency with user experience. Study 2 highlighted that Action-level Restriction, which permits overlapping selections but restricts concurrent identical operations, achieved better performance compared to exclusive object locking. Reactive strategies using averaging provided smooth collaboration for experienced users, while second-user priority enabled quick corrections. Our findings indicate that optimal strategy selection depends on task requirements, user expertise, and collaboration patterns. Based on the findings, we provide design implications for developing VR collaboration systems that support flexible sub-components manipulation while maintaining collaborative awareness and minimizing conflicts.

Problem

• co-manipulation
• conflict resolution
• virtual reality
• non-disjoint selection
• collaborative interaction

Innovation

• co-manipulation
• conflict resolution
• non-disjoint selection
• virtual reality collaboration
• sub-component interaction