The proliferation of XR devices has made egocentric hand pose estimation a vital task, yet this perspective is inherently challenged by frequent finger occlusions. To address this, we propose a novel approach that leverages the rich information in dorsal hand skin deformation, unlocked by recent advances in dense visual featurizers. We introduce a dual-stream delta encoder that learns pose by contrasting features from a dynamic hand with a baseline relaxed position. Our evaluation demonstrates that, using only cropped dorsal images, our method reduces the Mean Per Joint Angle Error (MPJAE) by 18% in self-occluded scenarios (fingers \(\ge 50\%\) occluded) compared to state-of-the-art techniques that depend on the whole hand’s geometry and large model backbones. Consequently, our method not only enhances the reliability of downstream tasks like index finger pinch and tap estimation in occluded scenarios but also unlocks new interaction paradigms, such as detecting isometric force for a surface “click” without visible movement while minimizing model size. Our codebase is found at https://github.com/hilab-open-source/deltadorsal.

We present VisionClaw, an always-on wearable AI agent that integrates live egocentric perception with agentic task execution. Running on Meta Ray-Ban smart glasses, VisionClaw continuously perceives real-world context and enables in-situ, speech-driven action initiation and delegation via OpenClaw AI agents. Therefore, users can directly execute tasks through the smart glasses, such as adding real-world objects to an Amazon cart, generating notes from physical documents, receiving meeting briefings on the go, creating events from posters, or controlling IoT devices. We evaluate VisionClaw through a controlled laboratory study (N=12) and a longitudinal deployment study (N=5). Results show that integrating perception and execution enables faster task completion and reduces interaction overhead compared to non-always-on and non-agent baselines. Beyond performance gains, deployment findings reveal a shift in interaction: tasks are initiated opportunistically during ongoing activities, and execution is increasingly delegated rather than manually controlled. These results suggest a new paradigm for wearable AI agents, where perception and action are continuously coupled to support situated, hands-free interaction.

Interacting with real-world objects in AR is difficult, especially when targets are distant, cluttered, or occluded. These challenges are amplified on emerging lightweight AR glasses, which often lack binocular or large field of view on display, but also continuous inputs, such as hand or eye tracking. Proxy-based interfaces offer an alternative by allowing users to interact with virtual abstractions of physical objects that can be repositioned, reorganized, and adapted to the task and device. However, designing such interfaces is currently manual and highly device-specific. We present Generative Proxy, a method for automatically generating proxy-based interfaces from three specifications: scene, intent, and device capabilities. We formulate generation as a constrained synthesis problem that first produces valid interfaces for the target device and task, then ranks candidates using semantic and articulatory distance inspired by direct manipulation theory. We demonstrate Generative Proxy across diverse scenes, device profiles, and user intents. Expert evaluation shows initial evidence that generated proxy UIs are useful and usable, highlighting proxy-based abstraction as a promising interaction paradigm for future AR glasses.

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.

Audio-only walking navigation can leave users disoriented, relying on vague cardinal directions and lacking real-time environmental context, leading to frequent errors. To address this, we present a novel system that integrates a Vision Language Model (VLM) with a spatial audio cue. Our system extracts environmental landmarks to anchor navigation instructions and, crucially, provides a directional spatial audio signal when the user faces the wrong direction, indicating the precise turn direction. In a user study (n=12), the spatial audio cue with VLM reduced route deviations compared to both VLM-only and Google Maps (audio-only) baseline systems. Users reported that the spatial audio cue effectively supported orientation and that landmark-anchored instructions provided a better navigation experience over audio-only Google Maps. This work serves as an initial look at the utility of future audio-only navigation systems for incorporating directional cues, especially real-time corrective spatial audio.

Most XR web browsers still present webpages as a single floating window, carrying over desktop design assumptions into immersive space. We explore an alternative by breaking the browser window and distributing a webpage into spatial UI chunks within a mixed-reality workspace. We present Break-the-Window (BTW), an exploratory prototype that spatially decomposes live, fully functional webpages into movable panels supporting mid-air and surface-attached placement, as well as direct touch and ray-based interaction. Through a formative study with XR practitioners and an exploratory qualitative study with 15 participants, we observed how spatial decomposition supports distributed attention and spatial meaning-making, while also surfacing challenges around coordination effort, interaction precision, and the lack of shared spatial UI conventions. This work invites discussion on how web interfaces might be reimagined for spatial computing beyond the single-window paradigm.

The gaze-and-pinch framework offers a high-fidelity interaction modality for spatial computing in virtual reality (VR), yet it remains vulnerable to coordination errors—timing misalignments between gaze fixation and pinch gestures. These errors are categorized into two types: late triggers (gaze leaves a target before pinch) and early triggers (pinch before gaze arrival on target). While late triggers are well-studied, early triggers lack robust solutions. We investigate two heuristics—Sticky selection (temporal buffer) and Magnetic selection (spatial field)—to mitigate these errors. A within-subjects study (N = 9) on the Samsung Galaxy XR evaluated these heuristics against a baseline. Findings indicate that while throughput and selection time remained stable, the heuristics fundamentally shifted user behavior and significantly reduced errors during selection. Notably, Magnetic selection induced an “offloading” effect where users traded precision for speed. Additionally, the heuristics reclassified ambiguous failures as explainable coordination errors. We provide recommendations for selection heuristics that enhance interaction speed and cognitive agency in virtual reality.

Eye gaze has become an essential input for spatial computing, but its coarse targeting and saccadic nature limit precision and complicate continuous interactions such as dragging, especially under user motion. Gaze+pinch has also become standard in XR for its convenience, yet mid-air gestures remain imprecise, fatiguing, and socially unacceptable. These limitations underscore the need for an approach that preserves the speed of gaze while enabling stable, fine control. We present GazeTune, a cascaded multimodal interaction technique combining gaze and touch to refine gaze-based selection and manipulation. Touch serves as a refinement channel within gaze pointing, allowing precise cursor and target control. Our work investigates how gaze-and-touch enhances dragging and mitigates Motion-Induced instability. In a study (N=20), we compared GazeTune against gaze-only and gaze-pinch methods in 2D dragging. Results show that GazeTune achieves significantly lower error with comparable execution time, validating its effectiveness and balanced trade-off between time and accuracy.

As Extended Reality (XR) systems increasingly map and understand the physical world, interacting with these blended representations remains challenging. The current push for "natural" inputs has its trade-offs: touch is limited by human reach and fatigue, while gaze often lacks the precision for fine interaction. To bridge this gap, we introduce World Mouse, a cross-reality cursor that reinterprets the familiar 2D desktop mouse for complex 3D scenes. The system is driven by two core mechanisms: within-object interaction, which uses surface normals for precise cursor placement, and between-object navigation, which leverages interpolation to traverse empty space. Unlike previous virtual-only approaches, World Mouse leverages semantic segmentation and mesh reconstruction to treat physical objects as interactive surfaces. Through a series of prototypes, including object manipulation and screen-to-world transitions, we illustrate how cross-reality cursors may enable seamless interactions across real and virtual environments.

Bridging the physical and digital world through interaction remains a core challenge in augmented reality (AR). Existing systems target single objects, limiting support for planning, comparison, and assembly tasks that depend on relationships among multiple items. We present Semantic Reality, an AR system focused on surfacing inter-object connectivity and making it interactive. Leveraging multimodal reasoning, spatial anchoring, and physical action recognition, Semantic Reality maintains a persistent model of objects around the user and their relationships. Connections are visualized in-situ to highlight compatibility, reveal next steps, and reduce ambiguity during tasks. We contribute a connectivity-centered interaction paradigm and a system architecture that couples anchor tracking, action sensing, and model inference to construct a live connectivity graph. In an exploratory study comparing Semantic Reality to a single-object baseline, participants reported clearer inter-object understanding and higher engagement and satisfaction, without increased workload. A scenario study illustrates where connectivity aids planning, sequencing, and disambiguation.

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.

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.

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.

Spatial interaction in 3D environments requires balancing efficiency and precision, which requires dynamic tracking speed adjustments. However, existing techniques often couple tracking speed adjustments directly with hand movements, reducing interaction flexibility. Inspired by the natural friction control inherent in the physical world, we introduce ForcePinch, a novel force-responsive spatial interaction method that enables users to intuitively modulate pointer tracking speed and smoothly transition between rapid and precise movements by varying their pinching force. To implement this concept, we developed a hardware prototype integrating a pressure sensor with a customizable mapping function that translates pinching force into tracking speed adjustments. We conducted a user study with 20 participants performing well-established 1D, 2D, and 3D object manipulation tasks, comparing ForcePinch against the distance-responsive technique Go-Go and speed-responsive technique PRISM. Results highlight distinctive characteristics of the force-responsive approach across different interaction contexts. Drawing on these findings, we highlight the contextual meaning and versatility of force-responsive interactions through four illustrative examples, aiming to inform and inspire future spatial interaction design.

Hand raycasting is widely used in extended reality (XR) for selection and interaction, but prolonged use can lead to arm fatigue (e.g., "gorilla arm"). Traditional techniques often require a large range of motion where the arm is extended and unsupported, exacerbating this issue. In this paper, we explore hand raycast techniques aimed at reducing arm fatigue, while minimizing impact to precision selection. In particular, we present Joint-Amplified Raycasting ( JAR) - a technique which scales and combines the orientations of multiple joints in the arm to enable more ergonomic raycasting. Through a comparative evaluation with the commonly used industry standard Shoulder-Palm Raycast (SP) and two other ergonomic alternatives-Offset Shoulder-Palm Raycast (OSP) and Wrist-Palm Raycast (WP)-we demonstrate that JAR results in higher selection throughput and reduced fatigue. A follow-up study highlights the effects of different JAR joint gains on target selection and shows users prefer JAR over SP in a representative UI task.

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.

Eye-based interaction techniques for extended reality, such as gaze and pinch, are simple to use however suffer from input precision issues. We present H2E, an integrated fine and coarse-grained pointing framework that cascades Hand, Head, and Eye inputs. We further introduce the MagicPinch gesture as an example of the framework, which allows for smooth transitioning between fine and coarse-grained pointing. When combined together, after users initiate a pinch gesture, a cursor appears midway during the pinch at the position of the gaze, which can be dragged by head pointing if needed before pinch confirmation. This has the advantage that it can add a precision component without changing the semantics of the technique. In this paper, we describe the design of the H2E framework and implementation of the MagicPinch technique. Furthermore, we present an evaluation of our method in a Fitts-based user study, exploring the speed-accuracy trade-offs against a gaze and pinch interaction baseline.

We revisit Bolt's classic "Put-That-There" concept for modern head-mounted displays by pairing Large Language Models (LLMs) with XR sensor and tech stack. The agent fuses (i) a semantically segmented 3-D environment, (ii) live application metadata, and (iii) users' verbal, pointing, and head-gaze cues to issue JSON window-placement actions. As a result, users can manage a panoramic workspace through: (1) explicit commands ("Place Google Maps on the coffee table"), (2) deictic speech plus gestures ("Put that there"), or (3) high-level goals ("I need to send a message"). Unlike traditional explicit interfaces, our system supports one-to-many action mappings and goal-centric reasoning, allowing the LLM to dynamically infer relevant applications and layout decisions, including interrelationships across tools. This enables seamless, intent-driven interaction without manual window juggling in immersive XR environments.

Innovations in spatial computing and artificial intelligence (AI) are making it possible to overlay dynamic, interactive digital elements on the physical world. Soon, every object might have a real-time digital twin, enabling the “Internet of Things” so as to identify and interact with even unconnected items. This programmable reality would enable computational manipulation of the world around us through alteration of its appearance or functionality, similar to software, but for reality itself. Advances in AI language models have enabled zero-shot segmentation and understanding of the world, making it possible to query and manipulate objects with precision. However, this vision also demands natural and intuitive ways for humans to interact with these models through gestures, gaze, and existing devices. Augmented reality (AR) provides the ideal bridge between AI output and human input in the physical world. Moreover, diffusion models and physics simulations offer exciting possibilities for content generation and editing, allowing us to transform everyday activities into extraordinary experiences. As AR devices become ubiquitous and indistinguishable from reality, these technologies blur the lines between reality and simulations. This raises profound questions about how we perceive and experience the world while having implications for memory, learning, and even behavior. Programmable reality enabled by AR and AI has vast potential to reshape our relationships with the digital realm, ultimately making it an extension of the physical realm.

Multiuser, multi-device environments in extended realities (XR) enable synchronous social interactions. With the freedom and flexibility to choose the most suitable device, we allow for inclusive environments where even spectators can be involved. However, existing research has mostly been conducted in controlled laboratory settings, which limits the applicability of the findings to naturalistic scenarios. We conducted a mixed methods study with social XR experts to explore situated and asymmetrical modalities in the context of XR gaming for enabling social interactions in naturalistic social settings, focusing on two games. We considered variations in available devices, spatial constraints, and users’ motivations and expertise. Our research suggests that asymmetrical interfaces may reduce barriers to entry for XR, support social connection, and promote cross-platform communication and collaboration. Together, our findings provoke critical discussions for future work on the effective deployment of asymmetrical interfaces in naturalistic scenarios and address potential technical, spatial, and social challenges.