Effortless manipulation informs and relies on preshaping: the subconscious posing of the hand before grasping. Virtual environments and the design of interaction techniques alters interaction requirements like contact and reach, forcing behavioural adaptation. We present a comparative study investigating preshaping behaviour across direct versus indirect (gaze-assisted) and bare-hand versus controller techniques on a docking task. Results reveal that response patterns scale with anticipated task difficulty, and that direct techniques elicit effective posing of the hand. Indirect techniques shortcut hand transport and, in turn lacks the sensory feedback to guide planning, inducing efficient but attenuated responses that necessitate compensatory manipulation and clutching. Notably, controllers that afford in-hand rotation allow users to extend their range of motion. These findings can inform interaction design to better afford preshaping and optimise 3D manipulation tasks.

Advances in emerging technologies, such as on-body mechanical actuators and electrical muscle stimulation, have allowed computers to take control over our bodies. This presents opportunities as well as challenges, raising fundamental questions about agency and the role of our bodies when interacting with technology. To advance this research field as a whole, we brought together expert perspectives in a week-long seminar to articulate the grand challenges that should be tackled when it comes to the design of computers’ control over our bodies. These grand challenges span technical, design, user, and ethical aspects. By articulating these grand challenges, we aim to begin initiating a research agenda that positions bodily control not only as a technical feature but as a central, experiential, and ethical concern for future human–computer interaction endeavors.

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.

Text entry for extended reality (XR) is far from perfect, and a variety of text entry techniques (TETs) have been proposed to fit various contexts of use. However, comparing between TETs remains challenging due to the lack of a consolidated collection of techniques, and limited understanding of how interaction attributes of a technique (e.g., presence of visual feedback) impact user performance. To address these gaps, this paper examines the current landscape of XR TETs by creating a database of 176 different techniques. We analyze this database to highlight trends in the design of these techniques, the metrics used to evaluate them, and how various interaction attributes impact these metrics. We discuss implications for future techniques and present TEXT: Text Entry for XR Trove, an interactive online tool to navigate our database.

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.

This paper investigates multi-selection in XR interfaces based on eye and hand interaction. We propose enabling multi-selection using different variations of techniques that combine gaze with a semi-pinch gesture, allowing users to select multiple objects, while on the way to a full-pinch. While our exploration is based on the semi-pinch mode for activating a quasi-mode, we explore four methods for confirming subselections in multi-selection mode, varying in effort and complexity: dwell-time (SemiDwell), swipe (SemiSwipe), tilt (SemiTilt), and non-dominant hand input (SemiNDH), and compare them to a baseline technique. In the user study, we evaluate their effectiveness in reducing task completion time, errors, and effort. The results indicate the strengths and weaknesses of each technique, with SemiSwipe and SemiDwell as the most preferred methods by participants. We also demonstrate their utility in file managing and RTS gaming application scenarios. This study provides valuable insights to advance 3D input systems in XR.

Interacting with real-world objects in Mixed Reality (MR) often proves difficult when they are crowded, distant, or partially occluded, hindering straightforward selection and manipulation. We observe that these difficulties stem from performing interaction directly on physical objects, where input is tightly coupled to their physical constraints. Our key insight is to decouple interaction from these constraints by introducing proxies-abstract representations of real-world objects. We embody this concept in Reality Proxy, a system that seamlessly shifts interaction targets from physical objects to their proxies during selection. Beyond facilitating basic selection, Reality Proxy uses AI to enrich proxies with semantic attributes and hierarchical spatial relationships of their corresponding physical objects, enabling novel and previously cumbersome interactions in MR - such as skimming, attribute-based filtering, navigating nested groups, and complex multi object selections - all without requiring new gestures or menu systems. We demonstrate Reality Proxy's versatility across diverse scenarios, including office information retrieval, large-scale spatial navigation, and multi-drone control. An expert evaluation suggests the system's utility and usability, suggesting that proxy-based abstractions offer a powerful and generalizable interaction paradigm for future MR systems.

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.

Producing quality segmentation masks for images is a fundamental problem in computer vision. Recent research has explored large-scale supervised training to enable zero- shot transfer segmentation on virtually any image style and unsupervised training to enable segmentation without dense annotations. However, constructing a model capable of segmenting anything in a zero-shot manner without any anno-tations is still challenging. In this paper, we propose to uti-lize the self-attention layers in stable diffusion models to achieve this goal because the pre-trained stable diffusion model has learned inherent concepts of objects within its attention layers. Specifically, we introduce a simple yet ef-fective iterative merging process based on measuring KL divergence among attention maps to merge them into valid segmentation masks. The proposed method does not re-quire any training or language dependency to extract qual-ity segmentation for any images. On COCO-Stuff-27, our method surpasses the prior unsupervised zero-shot trans-fer SOTA method by an absolute 26% in pixel accuracy and 17% in mean IoU. The project page is at https://sites.google.com/view/diffseg/home.11Georgia Institute of Technology

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.

Via Research through Design (RtD), we explore the potential of headphones as a general-purpose input device for both foreground motion-gestures as well as background sensing of user activity. As a familiar wearable device, headphones offer a compelling site for head-situated interaction and sensing. Using emerging sensing modalities such as inertial motion, capacitive touch sensing, and depth cameras, our implemented prototypes explore sensing and interaction techniques that offer a range of compelling capabilities.

Embodiment toward an avatar in virtual reality (VR) is generally stronger when there is a high degree of alignment between the user’s and self-avatar’s motion. However, one-to-one mapping between the two is not always ideal when user interacts with the virtual environment. On these occasions, the user input often leads to unnatural behavior without physical realism (e.g., objects penetrating virtual body, body unmoved by hitting stimuli). We investigate how adding physics correction to self-avatar motion impacts embodiment. Physics-aware self-avatar preserves the physical meaning of the movement but introduces discrepancies between the user’s and self-avatar’s motion, whose contingency is a determining factor for embodiment. To understand its impact, we conducted an in-lab study (n = 20) where participants interacted with obstacles on their upper bodies in VR with and without physics correction. Our results showed that, rather than compromising embodiment level, physics-responsive self-avatar improved embodiment compared to no-physics condition in both active and passive interactions.

While avatars have grown in popularity in social settings, their use in the workplace is still debatable. We conducted a large-scale survey to evaluate knowledge worker sentiment towards avatars, particularly the effects of realism on their acceptability for work meetings. Our survey of 2509 knowledge workers from multiple countries rated five avatar styles for use by managers, known colleagues and unknown colleagues. In all scenarios, participants favored higher realism, but fully realistic avatars were sometimes perceived as uncanny. Less realistic avatars were rated worse when interacting with an unknown colleague or manager, as compared to a known colleague. Avatar acceptability varied by country, with participants from the United States and South Korea rating avatars more favorably. We supplemented our quantitative findings with a thematic analysis of open-ended responses to provide a comprehensive understanding of factors influencing work avatar choices. In conclusion, our results show that realism had a significant positive correlation with acceptability. Non-realistic avatars were seen as fun and playful, but only suitable for occasional use.

The 2022 IEEE VGTC Significant New Researcher Award goes to Mar Gonzalez-Franco of Microsoft, in recognition of her research and incipient leadership in the field of Virtual Reality and spatial computing. Her work has provided new insights on how people behave and perceive avatars, haptics, sound and multisensory signals inside Virtual Reality. She has produced numerous new prototypes advancing the state of the art on haptic controllers and haptic displays. She has been prolific in proposing new theories and standardized methods of evaluation for different phenomena such as embodiment, locomotion, the uncanny valley of haptics, avatar self-recognition and the self-avatar follower effect. Meanwhile, Dr. Gonzalez-Franco has helped democratize access to VR technology by open sourcing of avatars and avatar animation libraries (Microsoft Rocketbox).

Numerous techniques have been proposed for locomotion in virtual reality (VR). Several taxonomies consider a large number of attributes (e.g., hardware, accessibility) to characterize these techniques. However, finding the appropriate locomotion technique (LT) and identifying gaps for future designs in the high-dimensional space of attributes can be quite challenging. To aid analysis and innovation, we devised Locomotion Vault (https://locomotionvault.github.io/), a database and visualization of over 100 LTs from academia and industry. We propose similarity between LTs as a metric to aid navigation and visualization. We show that similarity based on attribute values correlates with expert similarity assessments (a method that does not scale). Our analysis also highlights an inherent trade-off between simulation sickness and accessibility across LTs. As such, Locomotion Vault shows to be a tool that unifies information on LTs and enables their standardization and large-scale comparison to help understand the space of possibilities in VR locomotion.

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.

HapticBots introduces a novel encountered-type haptic approach for Virtual Reality (VR) based on multiple tabletop-size shape-changing robots. These robots move on a tabletop and change their height and orientation to haptically render various surfaces and objects on-demand. Compared to previous encountered-type haptic approaches like shape displays or robotic arms, our proposed approach has an advantage in deployability, scalability, and generalizability—these robots can be easily deployed due to their compact form factor. They can support multiple concurrent touch points in a large area thanks to the distributed nature of the robots. We propose and evaluate a novel set of interactions enabled by these robots which include: 1) rendering haptics for VR objects by providing just-in-time touch-points on the user’s hand, 2) simulating continuous surfaces with the concurrent height and position change, and 3) enabling the user to pick up and move VR objects through graspable proxy objects. Finally, we demonstrate HapticBots with various applications, including remote collaboration, education and training, design and 3D modeling, and gaming and entertainment.

Headworn Augmented Reality (AR) and Virtual Reality (VR) displays are an exciting new medium for locative storytelling. Authors face challenges planning and testing the placement of story elements when the story is experienced in multiple locations or the environment is large or complex. We present Story CreatAR, the first locative AR/VR authoring tool that integrates spatial analysis techniques. Story CreatAR is designed to help authors think about, experiment with, and reflect upon spatial relationships between story elements, and between their story and the environment. We motivate and validate our design through developing different locative AR/VR stories with several authors.

In this paper we present evidence that human perception of grasp might be most dependent on the information retrieved during the inward latch rather than the release of objects. This research is motivated by a number of haptic simulations and devices and grounded in perception science. We ran a user study (n=12) with two devices one capable of delivering compliant simulations for both grip and release (CLAW), i.e. symmetric device; the other only capable of delivering adaptive grip simulations (CapstanCrunch), i.e. asymmetric device. We fund that both performed similarly well for realism scores in a grasping task with objects of different stiffness. That similar performance was despite CapstanCrunch release was delivered by a constant spring independently of the compliance of the object. Our results show preliminary evidence that when simulating haptic grasp the release might be less important. And we propose a new theory of asymmetry of grasp in haptic perception.

We present PIVOT, a wrist-worn haptic device that renders virtual objects into the user's hand on demand. Its simple design comprises a single actuated joint that pivots a haptic handle into and out of the user's hand, rendering the haptic sensations of grasping, catching, or throwing an object anywhere in space. Unlike existing hand-held haptic devices and haptic gloves, PIVOT leaves the user's palm free when not in use, allowing users to make unencumbered use of their hand. PIVOT also enables rendering forces acting on the held virtual objects, such as gravity, inertia, or air-drag, by actively driving its motor while the user is firmly holding the handle. When wearing a PIVOT device on both hands, they can add haptic feedback to bimanual interaction, such as lifting larger objects. In our user study, participants (n=12) evaluated the realism of grabbing and releasing objects of different shape and size with mean score 5.19 on a scale from 1 to 7, rated the ability to catch and throw balls in different directions with different velocities (mean=5.5), and verified the ability to render the comparative weight of held objects with 87% accuracy for ~100g increments.

When embodying a virtual avatar in immersive VR applications where body tracking is enabled, users typically are and feel in control the avatar movements. However, there are situations in which the technology could be tweaked to flip this relationship so that an embodied avatar could affect the user’s motor behavior without users noticing it. This has been shown in action retargeting applications and motor contagion experiments. Here we discuss a different way in which an embodied avatar could implicitly drive users movements: the self-avatar follower effect. We review previous evidences and present new experimental results showing how, whenever the virtual body does not overlay with their physical body, users tend to unconsciously follow their avatar, filling the gap if the system allows for it. We discuss this effect in the context of the relevant neuroscientific literature, and propose a theoretical account of the follower effect at the intersection of motor control and inference theories.

Measuring Visual Latency in VR and AR devices has become increasingly complicated as many of the components will influence others in multiple loops and ultimately affect the human cognitive and sensory perception. In this paper we present a new method based on the idea that the performance of humans on a rapid motor task will remain constant, and that any added delay will correspond to the system latency. We ask users to perform a task inside different video see-through devices and also in front of a computer. We also calculate the latency of the systems using a hardware instrumentation-based measurement technique for bench-marking. Results show that this new form of latency measurement through human cognitive performance can be reliable and comparable to hardware instrumentation-based measurement. Our method is adaptable to many forms of user interaction. It is particularly suitable for systems, such as AR and VR, where externalizing signals is difficult, or where it is important to measure latency while the system is in use by a user.

No abstract available.

Through avatar embodiment in Virtual Reality (VR) we can achieve the illusion that an avatar is substituting our body: the avatar moves as we move and we see it from a first person perspective. However, self-identification, the process of identifying a representation as being oneself, poses new challenges because a key determinant is that we see and have agency in our own face. Providing control over the face is hard with current HMD technologies because face tracking is either cumbersome or error prone. However, limited animation is easily achieved based on speaking. We investigate the level of avatar enfacement, that is believing that a picture of a face is one's own face, with three levels of facial animation: (i) one in which the facial expressions of the avatars are static, (ii) one in which we implement lip-sync motion and (iii) one in which the avatar presents lip-sync plus additional facial animations, with blinks, designed by a professional animator. We measure self-identification using a face morphing tool that morphs from the face of the participant to the face of a gender matched avatar. We find that self-identification on avatars can be increased through pre-baked animations even when these are not photorealistic nor look like the participant.

Advances in tracking technology and wireless headsets enable walking as a means of locomotion in Virtual Reality. When exploring virtual environments larger than room-scale, it is often desirable to increase users' perceived walking speed, for which we investigate three methods. (1) Ground-Level Scaling increases users' avatar size, allowing them to walk farther. (2) Eye-Level Scaling enables users to walk through a World in Miniature, while maintaining a street-level view. (3) Seven-League Boots amplifies users' movements along their walking path. We conduct a study comparing these methods and find that users feel most embodied using Ground-Level Scaling and consequently increase their stride length. Using Seven-League Boots, unlike the other two methods, diminishes positional accuracy at high gains, and users modify their walking behavior to compensate for the lack of control. We conclude with a discussion on each technique's strength and weaknesses and the types of situation they might be appropriate for.

Recent hand-held controllers have explored a variety of haptic feedback sensations for users in virtual reality by producing both kinesthetic and cutaneous feedback from virtual objects. These controllers are grounded to the user's hand and can only manipulate objects through arm and wrist motions, not using the dexterity of their fingers as they would in real life. In this paper, we present TORC, a rigid haptic controller that renders virtual object characteristics and behaviors such as texture and compliance. Users hold and squeeze TORC using their thumb and two fingers and interact with virtual objects by sliding their thumb on TORC's trackpad. During the interaction, vibrotactile motors produce sensations to each finger that represent the haptic feel of squeezing, shearing or turning an object. Our evaluation showed that using TORC, participants could manipulate virtual objects more precisely (e.g., position and rotate objects in 3D) than when using a conventional VR controller.

Creating or arranging objects at runtime is needed in many virtual reality applications, but such changes are noticed when they occur inside the user's field of view. We present Mise-Unseen, a software system that applies such scene changes covertly inside the user's field of view. Mise-Unseen leverages gaze tracking to create models of user attention, intention, and spatial memory to determine if and when to inject a change. We present seven applications of Mise-Unseen to unnoticeably modify the scene within view (i) to hide that task difficulty is adapted to the user, (ii) to adapt the experience to the user's preferences, (iii) to time the use of low fidelity effects, (iv) to detect user choice for passive haptics even when lacking physical props, (v) to sustain physical locomotion despite a lack of physical space, (vi) to reduce motion sickness during virtual locomotion, and (vii) to verify user understanding during story progression. We evaluated Mise-Unseen and our applications in a user study with 15 participants and find that while gaze data indeed supports obfuscating changes inside the field of view, a change is rendered unnoticeably by using gaze in combination with common masking techniques.

We introduce CapstanCrunch, a force resisting, palm-grounded haptic controller that renders haptic feedback for touching and grasping both rigid and compliant objects in a VR environment. In contrast to previous controllers, Cap-stan¬Crunch renders human-scale forces without the use of large, high force, electrically power consumptive and ex-pensive actuators. Instead, CapstanCrunch¬ integrates a friction-based capstan-plus-cord variable-resistance brake mechanism that is dynamically controlled by a small inter-nal motor. The capstan mechanism magnifies the motor's force by a factor of around 40 as an output resistive force. Compared to active force control devices, it is low cost, low electrical power, robust, safe, fast and quiet, while providing high force control to user interaction. We describe the de-sign and implementation of CapstanCrunch and demon-strate its use in a series of VR scenarios. Finally, we evalu-ate the performance of CapstanCrunch in two user studies and compare our controller with an active haptic controller with the ability to simulate different levels of convincing object rigidity and/or compliance.

There are important individual differences when experiencing VR setups. We ran a study with 20 participants who got a scale-matched avatar and were asked to blind-walk to a VR target placed 2.5 meters away. In such setups, people typically underestimate distances by approximately 10% when virtual environments are viewed through head mounted displays. Consistent with previous studies we found that the underestimation was significantly reduced the more embodied the participants were. However, not all participants developed the same level of embodiment when exposed to the exact same conditions.

Under normal circumstances, our sense of touch is limited to our body. Recent evidence suggests, however, that our perception of touch can also be expanded to objects we are holding when certain tactile illusions are elicited by delivering vibrotactile stimuli in a particular manner. Here, we examined whether an extra-corporeal illusory sense of touch could be elicited using vibrotactile stimuli delivered via two independent handheld controllers while in virtual reality. Our results suggest that under the right conditions, one's sense of touch in space can be extended outside the body, and even into the empty space that surrounds us. Specifically, we show, in virtual reality, that one's sense of touch can be extended to a virtual stick one is holding, and also into the empty space between one's hands. These findings provide a means with which to expand the sense of touch beyond the hands in VR systems using two independent controllers, and also have important implications for our understanding of the human representation of touch.

Cardiography, quantitative measurement of the functioning of the heart, traditionally requires customized obtrusive contact sensors. Using new methods photoplethysmography and ballistocardiography signals can be captured using ubiquitous sensors, such as webcams and accelerometers. However, these signals are not visible to the unaided eye. We present Cardiolens - a mixed reality system that enables real-time, hands-free measurement and visualization of blood flow and vital signs from multiple people. The system combines a front-facing webcam, imaging ballistocardiography, and remote imaging photoplethysmography methods for recovering pulse signals. A heads up display allows users to view their own heart rate whenever they are wearing the device and the heart rate and heart rate variability of another person simply by looking at them. Cardiolens provides the wearer with a new way to understand physiological signals and has applications in human-computer interaction and in the study of social psychology.

Consumer 3D scanners and depth cameras are increasingly being used to generate content and avatars for Virtual Reality (VR) environments and avoid the inconveniences of hand modeling; however, it is sometimes difficult to evaluate quantitatively the mesh quality at which consumer available 3D scans should be exported, and whether the object perception might be affected by its shading. We propose using a paired-comparisons test based on psychophysics of perception to do that evaluation. As psychophysics is not subject to opinion, skill level, mental state, or economic situation it can be considered a quantitative way to measure how people perceive the mesh quality. In particular, we compare four different levels of mesh quality (1K, 5K, 10K and 20K triangles). We present two studies within subjects: in one we investigate the influences of seeing an object in a regular screen vs. in a Head Mounted Display (HMD); while in the second experiment we aim at detecting the effects of shading into quality perception. At each iteration of the pair-test comparisons participants pick the mesh that they think had higher quality; by the end of the experiment we compile a preference matrix. The results show a correlation between real and assessed quality, despite participants' reported uncertainty. We also find an interaction with quality and shading, which gains importance for quality perception when the mesh has high definition. Furthermore, we assess the subjective realism of the most/least preferred scans using an Immersive Augmented Reality (IAR) video-see-through setup to compare the real vs the 3D scanned object in the same HMD environment. Results show higher levels of realism were perceived through the HMD than when using a regular monitor, although the quality was similarly perceived in both systems.

This paper reports an experiment that investigated people's body ownership of an avatar that was observed in a virtual mirror. Twenty subjects were recruited in a within-groups study where 10 first experienced a virtual character that synchronously reflected their upper-body movements as seen in a virtual mirror, and then an asynchronous condition where the mirror avatar displayed prerecorded actions, unrelated to those of the participant. The other 10 subjects experienced the conditions in the opposite order. In both conditions the participant could carry out actions that led to elevation above ground level, as seen from their first person perspective and correspondingly in the mirror. A rotating virtual fan eventually descended to 2 m above the ground. The hypothesis was that synchronous mirror reflection would result in higher subjective sense of ownership. A questionnaire analysis showed that the body ownership illusion was significantly greater for the synchronous than asynchronous condition. Additionally participants in the synchronous condition avoided collision with the descending fan significantly more often than those in the asynchronous condition. The results of this experiment are put into context within similar experiments on multisensory correlation and body ownership within cognitive neuroscience.