Current Virtual Reality (VR) Head-Mounted Displays (HMDs) offer limited shortcuts for rapid command access, which often requires users to navigate menus through precise visual targeting at multiple depths. This process can be slow and distracting, particularly during immersive gaming or productivity tasks. While marking menus have shown effectiveness as a shortcut command access mechanism, their performance in VR has not been adequately studied. Moreover, their potential integration with 6-degree-of-freedom (6-DoF) controllers and 2-DoF joysticks in VR environments remains largely unexplored. In this paper, we introduce the Drum Menu, a bimanual shortcut command access technique derived from the idea of traditional pie menus, featuring three input methods, designed for 4-item and 8-item layouts specifically for VR controller command access. Users can select commands by rotating the joystick, drawing a stroke, or pointing in different directions. Bimanual input enables simultaneous access to two menu levels. A controlled user study reveals that drum menus are faster than the unimanual versions for the 4-item layout. Additionally, users prefer the bimanual joystick drum menu with the 4-item layout given its short task time, low error rate, and low physical movement. For the 8-item layout, stroke drum menus are found to be less error-prone for expert users compared to the other techniques.

Teleportation - changing the point of view in 3D space by specifying a position - is one of the most common locomotion solutions in VR. However, it currently lacks a mechanism to adjust the height in 3D space, and it is difficult for users to predict the exact final view after the teleportation. Users are relocated to a place without knowing what the final view will look like. As a result, they often need to perform remedial interactions to achieve their ideal position, which can be time-consuming and effort-intensive. In this paper, we present Fly the Moon to Me (Locomoontion), a novel technique that enables users to bring their destination to themselves through object manipulation. Users first create a copy of the object they want to approach as a preview by selecting it, then bring it to an ideal position and direction using existing object manipulation techniques, and then snap the original object to the preview together with the rest of the world. A controlled experiment with 18 participants via a teleportation task reveals that Locomoontion is more effective than the traditional Point&Teleport technique with grabbing the world as a remedy to adjust the final positioning.

Pointing is an elementary interaction in virtual and augmented reality environments, and, to effectively support selection, techniques must deal with the challenges of occlusion and depth specification. Most of the previous techniques require two explicit steps to handle occlusion. In this paper, we propose Conductor, an intuitive, plane-ray, intersection-based, 3D pointing technique where users leverage bimanual input to control a ray and intersecting plane. Conductor allows users to use the non-dominant hand to adjust the cursor distance on the ray while pointing with the dominant hand. We evaluate Conductor against Raycursor, a state-of-the-art VR pointing technique, and show that Conductor outperforms Raycursor for selection tasks. Given our results, we argue that bimanual selection techniques merit additional exploration to support object selection and placement within virtual environments.

One common task when controlling smart displays is the manipulation of menu items. Given current examples of smart displays that support distant bare hand control, in this paper we explore menu item selection tasks with three different mappings of barehand movement to target selection. Through a series of experiments, we demonstrate that Positional mapping is faster than other mappings when the target is visible but requires many clutches in large targeting spaces. Rate-based mapping is, in contrast, preferred by participants due to its perceived lower effort, despite being slightly harder to learn initially. Tradeoffs in the design of target selection in smart tv displays are discussed.

When automating tasks using some form of artificial intelligence, some inaccuracy in the result is virtually unavoidable. In many cases, the user must decide whether to try the automated method again, or fix it themselves using the available user interface. We argue this decision is influenced by both perceived automation accuracy and degree of task "controllability" (how easily and to what extent an automated result can be manually modified). This relationship between accuracy and controllability is investigated in a 750-participant crowdsourced experiment using a controlled, gamified task. With high controllability, self-reported satisfaction remained constant even under very low accuracy conditions, and overall, a strong preference was observed for using manual control rather than automation, despite much slower performance and regardless of very poor controllability.