This paper explores finger pressure as a continuous implicit input modality to enhance interaction precision in virtual reality (VR). While motion controllers are widely adopted, their limitations in delicate operations remain a critical challenge. We investigate whether finger pressure signals from conventional VR controllers could offer advantages over traditional kinematic metrics for precision interaction.Through empirical studies, we demonstrate a robust relationship between pressure dynamics and task precision requirements, leading to a lightweight sigmoid-based model that leverages detected pressure to infer desired control granularity. In a comparative evaluation of video-scrubbing tasks, our adaptive method outperforms static sensitivity baselines in both task performance and subjective preference, without elevating cognitive load. Further validation via a VR sketching application demonstrates that our technique maintains task performance while reducing mental demand compared to manual control. Our findings reveal the untapped potential of pressure-based input to bridge coarse and fine-grained VR interactions, offering a path toward more versatile and intuitive input systems.
Zhou et al. (2026) studied this question.