We explore the use of multimodal input to predict the landing position of a ray pointer while selecting targets in a virtual reality (VR) environment. We first extend a prior 2D Kinematic Template Matching technique to include head movements. This new technique, Head-Coupled Kinematic Template Matching, was found to improve upon the existing 2D approach, with an angular error of 10.0° when a user was 40% of the way through their movement. We then investigate two additional models that incorporated eye gaze, which were both found to further improve the predicted landing positions. The first model, Gaze-Coupled Kinematic Template Matching resulted in angular error of 6.8° for reciprocal target layouts and 9.1° for random target layouts, when a user was 40% of the way through their movement. The second model, Hybrid Kinematic Template Matching, resulted in angular error of 5.2° for reciprocal target layouts and 7.2° for random target layouts when a user was 40% of the way through their movement. We also found that using just the current gaze location resulted in sufficient predictions in many conditions. We reflect on our results by discussing the broader implications of utilizing multimodal input to inform selection predictions in VR.

This paper presents a new technique to predict the ray pointer landing position for selection movements in virtual reality (VR) environments. The technique adapts and extends a prior 2D kinematic template matching method to VR environments where ray pointers are used for selection. It builds on the insight that the kinematics of a controller and Head-Mounted Display (HMD) can be used to predict the ray's final landing position and angle. An initial study provides evidence that the motion of the head is a key input channel for improving prediction models. A second study validates this technique across a continuous range of distances, angles, and target sizes. On average, the technique's predictions were within 7.3° of the true landing position when 50% of the way through the movement and within 3.4° when 90%. Furthermore, compared to a direct extension of Kinematic Template Matching, which only uses controller movement, this head-coupled approach increases prediction accuracy by a factor of 1.8x when 40% of the way through the movement.