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.

翻译：虚拟现实（VR）协同操控允许多用户与共享的虚拟对象进行协作交互。然而，现有研究将对象视为单一整体，忽略了用户需要同时操控不同子组件的情况。本文针对用户在协同操控中选择重叠顶点（非交集集合）时的冲突解决方案进行研究。我们提出一个综合框架，包括预防性策略（对象级与动作级限制）和反应性策略（计算冲突解决）。通过两项包含76名参与者（38对）的用户研究，我们在协作线框编辑任务中评估了这些方法。研究一确定平均法为最优计算方法，平衡了任务效率与用户体验。研究二表明，允许重叠选择但限制并发相同操作的动作级限制策略，相较于独占对象锁定策略表现出更优性能。采用平均法的反应性策略可为经验用户提供流畅协作体验，而次用户优先机制则支持快速纠正。研究结果表明，最优策略选择取决于任务需求、用户专业水平及协作模式。基于研究发现，我们为开发支持灵活子组件操控、同时保持协作感知并最小化冲突的VR协作系统提供了设计建议。