Eric J. Gonzalez
Eric J. Gonzalez is a Research Scientist in the Blended Intelligence Research & Devices (BIRD) Lab, where he works on advancing human-computer interaction for extended reality (XR). His research is broadly focused on novel input & interaction techniques and real-time AI-mediated experiences, and has led to over 25 publications. Previously, Eric received his Ph.D. from Stanford University in 2022 and B.S. from the University of Florida in 2016, with research internships completed at Microsoft Research and Meta Reality Labs. His work has been recognized with multiple awards at top-tier HCI venues, including a SIGCHI Special Recognition for industry-academia collaboration.
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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 arbitrary 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. Our user study across 12 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.
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ForcePinch: Force-Responsive Spatial Interaction for Tracking Speed Control and Beyond
Chenyang Zhang
Tiffany S Ma
John Andrews
Yalong Yang
ACM UIST (2025)
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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 distance-responsive technique Go-Go and speed-responsive technique PRISM. Results highlight distinctive characteristics of the force-responsive approach across different interaction contexts. Drawing insights from these findings, we further propose a generative framework designed to systematically extend force-responsive interactions to diverse spatial interaction scenarios. To illustrate the versatility and practical applicability of this framework, we designed and implemented eight representative applications, offering guidelines and inspiration for future force-enabled interaction designs.
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Online-EYE: Multimodal Implicit Eye Tracking Calibration for XR
Baosheng James Hou
Lucy Abramyan
Prasanthi Gurumurthy
Khushman Patel
Haley Adams
Andrea Colaco
Ken Pfeuffer
Hans Gellersen
Karan Ahuja
2025
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Unlike other inputs for VR 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.
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We present HandOver, a new interaction technique designed to unify precise mouse‐based selection with intuitive hand‐tracking for virtual object manipulation. Our system’s depth‐aware 3D cursor allows users to join the accuracy of mouse input for near‐field tasks, then seamlessly switch to a “hand clone” when the physical hand lifts off the mouse area. This transition mechanism extends direct grasping and ray‐casting capabilities to distant objects, mitigating the fatigue and jitter often associated with purely gestural input. In a formal user study, we compare HandOver against two ray‐based techniques: Ray+Hand and Ray—across near, mid, and far distances. Quantitative results indicate that HandOver maintains minimal error and faster targeting times at far‐field ranges while offering fluid transitions into more distant interactions. Subjective feedback and ergonomic measures (e.g., RULA posture, NASA‐TLX workload) further reinforce that blending mouse precision with hands‐on expressiveness can improve user comfort, accuracy, and speed in immersive 3D environments. Bridging these paradigms yields a single, continuous workflow that adapts to different spatial contexts, reaffirming the role of “the hand” in modern virtual object manipulation.
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Beyond the Phone: Exploring Context-aware Interaction Between Mobile andMixed Reality Devices
Fengyuan Zhu
Daniel Kalmar
Mahdi Tayarani
2025
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Despite the surge in popularity of virtual reality (VR), mobile phones remain the primary medium for accessing digital content, offering both privacy and portability. This short paper presents Beyond the Phone, a novel framework that enhances mobile phones in VR with context-aware controls and spatial augmentation. We first establish a comprehensive design space through brainstorming and iterative discussions with VR experts. We then develop a proof-of-concept system that analyzes UI layouts to offer context-aware controls and spatial augmentation, targeting six key application areas within our design space. Finally, we demonstrate that our system can effectively adapt to a broad spectrum of applications at runtime, and discuss future directions with reviews with seven experts.
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XR devices running chatbots powered by Large Language Models (LLMs) have the potential to become always-on agents that significantly enhance productivity scenarios. Current screen-based chatbots fail to fully utilize the comprehensive 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 included in the query. We propose a solution that leverages an attention framework to implicitly derive context from user actions, eye gaze, and contextual memory within the XR environment. Our approach minimizes the need for explicitly engineered prompts, fostering intuitive and grounded interactions that provide deeper user insights for the chatbot.
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H2E: Hand, Head, Eye: A Multimodal Cascade of Natural Inputs
Khushman Patel
Hans Gellersen
Ken Pfeuffer
IEEE VR (2025)
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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, a fine and coarse-grained pointing technique that cascades Hand, Head, and Eye inputs. As users initiate a pinch gesture, a cursor appears at the gaze point that can be dragged by head pointing before pinch confirmation. This has the potential advantage that it can add a precision component without changing the semantics of the technique. In this paper, we describe the design and implementation of the 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.
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Interactions with Extended Reality Head Mounted Devices (XR HMDs) applications require precise, intuitive and efficient input methods. Current approaches either rely on power-intensive sensors, such as cameras for hand-tracking, or specialized hardware in the form of handheld controllers. As an alternative, past works have explored the use of devices already present with the user, in the form of smartphones and smartwatches as practical input solutions. However, this approach risks interaction overload---how can one determine whether the user’s interaction gestures on the watch-face or phone screen are directed toward control of the mobile device itself or the XR device? To this effect, we propose a novel framework for cross-device input routing and device arbitration by employing Inertial Measurement Units (IMUs) within these devices. We validate our approach in a user study with six participants. By making use of the relative orientation between the headset and the target input device, we can estimate the intended device of interaction with 93.7% accuracy. Our method offers a seamless, energy-efficient alternative for input management in XR, enhancing user experience through natural and ergonomic interactions.
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WindowMirror is a framework for using XR headsets in productivity scenarios. The toolkit provides users with a simulated, extended screen real-estate. It allows users to interact with multiple desktop applications in real-time within a XR environment. Our architecture has two main modules: one a Unity package and a Python backend, which makes it easy to use and extend. WindowMirror supports traditional desktop interaction methods such as mouse, keyboard, and hand tracking. Furthermore, it features a Cylindrical Window Layout, an emerging design pattern which is particularly effective for single-user, egocentric perspectives. The introduction of WindowMirror aims to set a foundation for future research in XR screen-focused productivity scenarios.
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Augmented Object Intelligence with XR-Objects
Mustafa Doga Dogan
Karan Ahuja
Andrea Colaco
Johnny Lee
Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology (UIST), ACM (2024), pp. 1-15
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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.
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