Mid-air gestures in Extended Reality (XR) often lead to fatigue, discomfort and imprecision, limiting their suitability for extended use. Surface-based interactions offer a compelling alternative, providing improved accuracy, speed, and comfort. However, current egocentric vision-based methods struggle with reliable surface inputs due to challenges in hand tracking and surface plane estimation from oblique and occluded viewing angles. To this extent, we introduce SurfaceXR, a novel sensor fusion approach that combines headset based hand tracking with micro-vibration data sampled from commodity smartwatch IMUs to enable precise and robust inputs on everyday surfaces. Our system is designed with flexibility in mind - it can function using only hand tracking, only IMU sensing, or optimally with both modalities combined, and remains robust even without explicit surface calibration. Our key insight is that these modalities are complementary - hand tracking provides 3D positional data of hand joints, whereas IMUs supply high-frequency wrist/hand motion data. Our user study across 21 participants validates SurfaceXR's effectiveness in augmenting surface touch tracking and 8 class hand-surface gesture recognition, demonstrating significant improvements over single-modality approaches. Enabled by SurfaceXR, we demonstrate a series of interactive apps for both AR and VR, ranging from on-surface sketching, text entry and gesture-based navigation.

Unlike other inputs for extended reality (XR) that work out of the box, eye tracking typically requires custom calibration per user or session. We present a multimodal inputs approach for implicit calibration of eye tracker in VR, leveraging UI interaction for continuous, background calibration. Our method analyzes gaze data alongside controller interaction with UI elements, and employing ML techniques it continuously refines the calibration matrix without interrupting users from their current tasks. Potentially eliminating the need for explicit calibration. We demonstrate the accuracy and effectiveness of this implicit approach across various tasks and real time applications achieving comparable eye tracking accuracy to native, explicit calibration. While our evaluation focuses on VR and controller-based interactions, we anticipate the broader applicability of this approach to various XR devices and input modalities.

We present HandOver, an extended reality (XR) interaction technique designed to unify the precision of traditional mouse input for object selection with the expressiveness of hand-tracking for object manipulation. With HandOver, the mouse is used to drive a depth-aware 3D cursor enabling precise and restful targeting -by hovering their hand over the mouse, the user can then seamlessly transition into direct 3D manipulation of the target object. In a formal user study, we compare HandOver against two raybased techniques: traditional raycasting (Ray) and a hybrid method (Ray+Hand) in a 3D docking task. Results show HandOver yields lower task errors across all distances, and moreover improves interaction ergonomics as highlighted by a RULA posture analysis and self-reported measures (NASA-TLX). These findings illustrate the benefits of blending traditional precise input devices with the expressive gestural inputs afforded by hand-tracking in XR, leading to improved user comfort and task performance. This blended paradigm yields a unified workflow allowing users to leverage the best of each input modality as they interact in immersive environments.

a) Fast Tracking Speed (b) Slow Tracking Speed Figure 1: ForcePinch is an object manipulation method that allows users to control the tracking speed of a mid-air pointer by modulating pinch force during object manipulation.(a) A light pinch results in fast tracking, supporting broad and rapid movements.(b) A heavy pinch produces slow tracking, allowing for fine-grained precision.

XR devices running chat-bots powered by Large Language Models (LLMs) have the to become always-on agents that enable much better productivity scenarios. Current screen based chat-bots do not take advantage of the the full-suite of natural inputs available in XR, including inward facing sensor data, instead they over-rely on explicit voice or text prompts, sometimes paired with multi-modal data dropped as part of the query. We propose a solution that leverages an attention framework that derives context implicitly from user actions, eye-gaze, and contextual memory within the XR environment. Our work minimizes the need for engineered explicit prompts, fostering grounded and intuitive interactions that glean user insights for the chat-bot.

Despite the growing prevalence of Extended Reality (XR) headsets, their integration with mobile phones remains limited. Existing approaches primarily replicate the phone’s interface in XR or use the phone solely as a 6DOF controller. This paper introduces a novel framework for seamless transitions among mirrored, magnified, and augmented views, dynamically adapts the interface with the content and state of mobile applications. To achieve this, we establish a design space through literature reviews and expert workshops, outline user journeys with common real-world applications, and develop a prototype system that automatically analyzes UI layouts to provide enhanced controls and spatial augmentation. We validate our prototype system with a user study to assess its adaptability to a broad spectrum of applications at runtime, reported its strengths and weaknesses, and suggest directions to advance the future adaption in Phone-XR integration.

Seamless integration of physical objects as interactive digital entities remains a challenge for spatial computing. This paper explores Augmented Object Intelligence (AOI) in the context of XR, an interaction paradigm that aims to blur the lines between digital and physical by equipping real-world objects with the ability to interact as if they were digital, where every object has the potential to serve as a portal to digital functionalities. Our approach utilizes real-time object segmentation and classification, combined with the power of Multimodal Large Language Models (MLLMs), to facilitate these interactions without the need for object pre-registration. We implement the AOI concept in the form of XR-Objects, an open-source prototype system that provides a platform for users to engage with their physical environment in contextually relevant ways using object-based context menus. This system enables analog objects to not only convey information but also to initiate digital actions, such as querying for details or executing tasks. Our contributions are threefold: (1) we define the AOI concept and detail its advantages over traditional AI assistants, (2) detail the XR-Objects system’s open-source design and implementation, and (3) show its versatility through various use cases and a user study.

Extended Reality (XR) systems with hand-tracking support direct manipulation of objects with both hands. A common interaction in this context is for the non-dominant hand (NDH) to orient an object for input by the dominant hand (DH). We explore bimanual interaction with gaze through three new modes of interaction where the input of the NDH, DH, or both hands is indirect based on Gaze+Pinch. These modes enable a new dynamic interplay between our hands, allowing flexible alternation between and pairing of complementary operations. Through applications, we demonstrate several use cases in the context of 3D modelling, where users exploit occlusion-free, low-effort, and fluid two-handed manipulation. To gain a deeper understanding of each mode, we present a user study on an asymmetric rotate-translate task. Most participants preferred indirect input with both hands for lower physical effort, without a penalty on user performance. Otherwise, they preferred modes where the NDH oriented the object directly, supporting preshaping of the hand, which is more challenging with indirect gestures. The insights gained are of relevance for the design of XR interfaces that aim to leverage eye and hand input in tandem.

Illusory VR interaction techniques such as hand redirection work because humans use vision to adjust their motor commands during movement (e.g., reaching). Existing simulations of redirected reaching are limited, however, and have not yet incorporated important stochastic characteristics like sensorimotor noise, nor captured redirection’s effect on movement duration. In this work, we propose adapting a stochastic optimal feedback control (SOFC) model of normal reach to simulate redirection by augmenting sensory feedback at run-time. We present a summary of our simulation and validate it against user data gathered in multiple redirection conditions. We also evaluate the impacts of visual attention on the effectiveness of redirection in real users and replicate the effects in simulation. Our results show that an infinite-horizon SOFC model is able to reproduce key characteristics of redirected reaches and highlight the benefits of SOFC as a tool for simulating, evaluating, and gaining insights about redirection techniques.

Reach redirection is an illusion-based virtual reality (VR) interaction technique where a user’s virtual hand is shifted during a reach in order to guide their real hand to a physical location. Prior works have not considered the underlying sensorimotor processes driving redirection. In this work, we propose adapting a sensorimotor model for goal-directed reach to obtain a model for visually-redirected reach, specifically by incorporating redirection as a sensory bias in the state estimate used by a minimum jerk motion controller. We validate and then leverage this model to develop a Model Predictive Control (MPC) approach for reach redirection, enabling the real-time generation of spatial warping according to desired optimization criteria (e.g., redirection goals) and constraints (e.g., sensory thresholds). We illustrate this approach with two example criteria – redirection to a desired point and redirection along a desired path – and compare our approach against existing techniques in a user evaluation.

X-Rings is a novel hand-mounted 360° shape display for Virtual Reality that renders objects in 3D and responds to user-applied touch and grasping force. Designed as a modular stack of motor-driven expandable rings (5.7-7.7 cm diameter), X-Rings renders radially-symmetric surfaces graspable by the user’s whole hand. The device is strapped to the palm, allowing the fingers to freely make and break contact with the device. Capacitance sensors and motor current sensing provide estimates of finger touch states and gripping force. We present the results of a user study evaluating participants’ ability to associate device-rendered shapes with visually-rendered objects as well as a demo application that allows users to freely interact with a variety of objects in a virtual environment.

In this article, we investigate the effects of active transient vibration and visuo-haptic illusion to augment the perceived softness of haptic proxy objects. We introduce a system combining active transient vibration at the fingertip with visuo-haptic illusions. In our hand-held device, a voice coil actuator transmits active transient vibrations to the index fingertip, while a force sensor measures the force applied on passive proxy objects to create visuo-haptic illusions in virtual reality. We conducted three user studies to understand both the vibrotactile effect and its combined effect with visuo-haptic illusions. A preliminary study confirmed that active transient vibrations can intuitively alter the perceived softness of a proxy object. Our first study demonstrated that those same active transient vibrations can generate different perceptions of softness depending on the material of the proxy object used. In our second study, we evaluated the combination of active transient vibration and visuo-haptic illusion, and found that both significantly influence perceived softness, with with the visuo-haptic effect being dominant. Our third study further investigated the vibrotactile effect while controlling for the visuo-haptic illusion. The combination of these two methods allows users to effectively perceive various levels of softness when interacting with haptic proxy objects.

The ability to effectively read and interpret tactile graphics and charts is an essential part of a tactile learner’s path to literacy, but is a skill that requires instruction and training. Many teachers of the visual impaired (TVIs) report that blind and visually impaired students have trouble interpreting graphics independently without individual instruction. We present PantoGuide, a low-cost system that provides audio and haptic guidance, via skin-stretch feedback to the dorsum of a user’s hand while the user explores a tactile graphic overlaid on a touchscreen. This system allows programming of haptic guidance patterns and cues for tactile graphics that can be experienced by students learning remotely or that can be reviewed by a student independently. We propose two teaching scenarios (synchronous and asynchronous) and two guidance interactions (point-to-point and continuous) that the device can support – and demonstrate their use in a set of applications we co-design with one co-author who is blind and a tactile graphics user.

Encountered-type haptic devices (EHDs) face a number of challenges when physically embodying content in a virtual environment, including workspace limits and device latency. To address these issues, we propose REACH+, a framework for dynamic visuo-haptic redirection to improve the perceived performance of EHDs during physical interaction in VR. Using this approach, we estimate the user's arrival time to their intended target and redirect their hand to a point within the EHD's spatio-temporally reachable space. We present an evaluation of this framework implemented with a desktop mobile robot in a 2D target selection task, tested at four robot speeds (20, 25, 30 and 35 cm/s). Results suggest that REACH+ can improve the performance of lower-speed EHDs, increasing their rate of on-time arrival to the point of contact by up to 25% and improving users? self-reported sense of realism.

Haptic retargeting is a virtual reality (VR) interaction technique enabling virtual objects to be ”remapped” to different haptic proxies by offsetting the user’s virtual hand from their physical hand. While researchers have investigated single-hand retargeting, the effects of bimanual interaction in the context of haptic retargeting have been less explored. In this study, we present an evaluation of perceptual detection rates for bimanual haptic retargeting in VR. We tested 64 combinations of simultaneous left- and right-hand retargeting ranging from − 24° to + 24° offsets and found that bimanual retargeting can be more noticeable to users when the hands are redirected in different directions as opposed to the same direction.

We explore interactions enabled by 2D spatial manipulation and self-actuation of a tabletop shape display. To explore these interactions, we developed shapeShift, a compact, high-resolution (7 mm pitch), mobile tabletop shape display. shapeShift can be mounted on passive rollers allowing for bimanual interaction where the user can freely manipulate the system while it renders spatially relevant content. shapeShift can also be mounted on an omnidirectional-robot to provide both vertical and lateral kinesthetic feedback, display moving objects, or act as an encountered-type haptic device for VR. We present a study on haptic search tasks comparing spatial manipulation of a shape display for egocentric exploration of a map versus exploration using a fixed display and a touch pad. Results show a 30% decrease in navigation path lengths, 24% decrease in task time, 15% decrease in mental demand and 29% decrease in frustration in favor of egocentric navigation.