Virtual reality often uses motion tracking to incorporate physical hand movements into interaction techniques for selection and manipulation of virtual objects. To increase realism and allow direct hand interaction, real-world physical objects can be aligned with virtual objects to provide tactile feedback and physical grasping. However, unless a physical space is custom configured to match a specific virtual reality experience, the ability to perfectly match the physical and virtual objects is limited. Our research addresses this challenge by studying methods that allow one physical object to be mapped to multiple virtual objects that can exist at different virtual locations in an egocentric reference frame. We study two such techniques: one that introduces a static translational offset between the virtual and physical hand before a reaching action, and one that dynamically interpolates the position of the virtual hand during a reaching motion. We conducted two experiments to assess how the two methods affect reaching effectiveness, comfort, and ability to adapt to the remapping techniques when reaching for objects with different types of mismatches between physical and virtual locations. We also present a case study to demonstrate how the hand remapping techniques could be used in an immersive game application to support realistic hand interaction while optimizing usability. Overall, the translational technique performed better than the interpolated reach technique and was more robust for situations with larger mismatches between virtual and physical objects.

The use of self avatars in virtual reality (VR) can bring users a stronger sense of presence and produce a more compelling experience by providing additional visual feedback during interactions. Avatars also become increasingly more relevant in VR as they provide a user with an identity for social interactions in multi-user settings. However, with current consumer VR setups that include only a head mounted display and hand controllers, implementation of self avatars are generally limited in the ability to mimic actions performed in the real world. Our work explores the idea of simulating a wide range of upper body motions using motion and positional data from only the head and hand motion data. We present a method to differentiate head and hip motions using information from captured motion data and applying corresponding changes to a virtual avatar. We discuss our approach and initial results.