Auditorily Embodied Conversational Agents: Effects of Spatialization and Situated Audio Cues on Presence and Social PerceptionEmbodiment can enhance conversational agents, such as increasing their perceived presence. This is typically achieved through visual representations of a virtual body; however, visual modalities are not always available, such as when users interact with agents using headphones or display-less glasses. In this work, we explore auditory embodiment. By introducing auditory cues of bodily presence — through spatially localized voice and situated Foley audio from environmental interactions — we investigate how audio alone can convey embodiment and influence perceptions of a conversational agent. We conducted a 2 (spatialization: monaural vs. spatialized) × 2 (Foley: none vs. Foley) within-subjects study, where participants (n=24) engaged in conversations with agents. Our results show that spatialization and Foley increase co-presence, but reduce users’ perceptions of the agent’s attention and other social attributes.2026YCYi Fei Cheng et al.Carnegie Mellon UniversityAffective Human-Computer DialogueSpatial Audio & 3D SoundAffective Feedback & Emotion Regulation InterfacesCHI
Simulating Human Audiovisual Search BehaviorLocating a target based on auditory and visual cues—such as finding a car in a crowded parking lot or identifying a speaker in a virtual meeting—requires balancing effort, time, and accuracy under uncertainty. Existing models of audiovisual search often treat perception and action in isolation, overlooking how people adaptively coordinate movement and sensory strategies. We present Sensonaut, a computational model of embodied audiovisual search. The core assumption is that people deploy their body and sensory systems in ways they believe will most efficiently improve their chances of locating a target, trading off time and effort under perceptual constraints. Our model formulates this as a resource-rational decision-making problem under partial observability. We validate the model against newly collected human data, showing that it reproduces both adaptive scaling of search time and effort under task complexity, occlusion, and distraction, and characteristic human errors. Our simulation of human-like resource-rational search informs the design of audiovisual interfaces that minimize search cost and cognitive load.2026HCHyunsung Cho et al.Aalto UniversityEye Tracking & Gaze InteractionSonification & Auditory DisplayAffective Feedback & Emotion Regulation InterfacesCHI
One Body, Two Minds: Alternating VR Perspective During Remote Teleoperation of Supernumerary LimbsRemote VR teleoperation with supernumerary robotic limbs enables distant users to operate in another’s local space. While a shared first-person view aids hand-eye coordination, locking the guest’s camera to the host’s head can degrade comfort, embodiment, and coordination. Based on a formative study (N=10) using a virtual supernumerary robotic limbs configuration to stress-test coordination, we propose guest-driven perspective switching from a shared first-person baseline (Shared Embodied View) to two alternatives: (a) a stabilized view with guest-controlled rotation (Embedded Anchored View), and (b) a fully decoupled third-person view (Out-of-body View). We ran a user study with 24 pairs (N=48), who switched between the baseline and proposed views as task demands changed. We measured performance, embodiment, fatigue, physiological arousal, and switching behaviors. Our results reveal role-dependent trade-offs: Out-of-body View improves navigation efficiency and reduces errors, while Embedded Anchored View supports embodiment. We conclude with guidelines: use Embedded Anchored View for hand-centric adjustments, Out-of-body View for navigation and object placement, and ensure smooth transitions.2026HZHongyu Zhou et al.The University of SydneyTeleoperation & TelepresenceSocial & Collaborative VRImmersion & Presence ResearchCHI
Sensing Noticeability in Ambient Information EnvironmentsDesigning notifications in Augmented Reality (AR) that are noticeable yet unobtrusive is challenging since achieving this balance heavily depends on the user’s context. However, current AR systems tend to be context-agnostic and require explicit feedback to determine whether a user has noticed a notification. This limitation restricts AR systems from providing timely notifications that are integrated with users’ activities. To address this challenge, we studied how sensors can infer users’ detection of notifications while they work in an office setting. We collected 98 hours of data from 12 users, including their gaze, head position, computer interactions, and engagement levels. Our findings showed that combining gaze and engagement data most accurately classified noticeability (AUC = 0.81). Even without engagement data, the accuracy was still high (AUC = 0.76). Our study also examines time windowing methods and compares general and personalized models.2025YCTingyu Cheng et al.Carnegie Mellon University, Human-Computer Interaction InstituteAR Navigation & Context AwarenessContext-Aware ComputingCHI
MiniMates: Miniature Avatars for AR Remote Meetings within Limited Physical SpacesRemote meetings using 3D avatars in Augmented Reality (AR) allow effective communication and enable users to retain awareness of their surroundings. However, positioning 3D avatars effectively and consistently for all users in AR is challenging since most spaces, such as offices or living rooms, are not large enough to accommodate multiple life-sized avatars without interference. To address this issue, we contribute MiniMates---a novel approach leveraging miniature avatars, which make it possible to place multiple remote users in a limited physical space. We see MiniMates as complementary to traditional 2D video conferencing and immersive telepresence. Our approach automatically adjusts the formation of avatars and redirects users' head and body orientation to facilitate communication. Results from our user study (n = 24) show that participants experience a higher sense of co-presence compared to video conferencing, and that MiniMates enabled them to communicate the direction of their interactions non-verbally as well as manage multiple simultaneous conversations.2025AKAkihiro Kiuchi et al.The University of TokyoSocial & Collaborative VRMixed Reality WorkspacesContext-Aware ComputingCHI
Persistent Assistant: Seamless Everyday AI Interactions via Intent Grounding and Multimodal FeedbackCurrent AI assistants predominantly use natural language interactions, which can be time-consuming and cognitively demanding, especially for frequent, repetitive tasks in daily life. We propose Persistent Assistant, a framework for seamless and unobtrusive interactions with AI assistants. The framework has three key functionalities: (1) efficient intent specification through grounded interactions, (2) seamless target referencing through embodied input, and (3) intuitive response comprehension through multimodal perceptible feedback. We developed a proof-of-concept system for everyday decision-making tasks, where users can easily repeat queries over multiple objects using eye gaze and pinch gesture, as well as receiving multimodal haptic and speech feedback. Our study shows that multimodal feedback enhances user experience and preference by reducing physical demand, increasing perceived speed, and enabling intuitive and instinctive human-AI assistant interaction. We discuss how our framework can be applied to build seamless and unobtrusive AI assistants for everyday persistent tasks.2025HCHyunsung Cho et al.Meta Inc., Reality Labs Research; Carnegie Mellon University, Human-Computer Interaction InstituteIn-Vehicle Haptic, Audio & Multimodal FeedbackVoice User Interface (VUI) DesignIntelligent Voice Assistants (Alexa, Siri, etc.)CHI
Towards Music-Aware Virtual AssistantsWe propose a system for modifying spoken notifications in a manner that is sensitive to the music a user is listening to. Spoken notifications provide convenient access to rich information without the need for a screen. Virtual assistants see prevalent use in hands-free settings such as driving or exercising, activities where users also regularly enjoy listening to music. In such settings, virtual assistants will temporarily mute a user's music to improve intelligibility. However, users may perceive these interruptions as intrusive, negatively impacting their music-listening experience. To address this challenge, we propose the concept of music-aware virtual assistants, where speech notifications are modified to resemble a voice singing in harmony with the user's music. We contribute a system that processes user music and notification text to produce a blended mix, replacing original song lyrics with the notification content. In a user study comparing musical assistants to standard virtual assistants, participants expressed that musical assistants fit better with music, reduced intrusiveness, and provided a more delightful listening experience overall.2024AWAlexander Wang et al.Voice User Interface (VUI) DesignIntelligent Voice Assistants (Alexa, Siri, etc.)Music Composition & Sound Design ToolsUIST
Auptimize: Optimal Placement of Spatial Audio Cues for Extended RealitySpatial audio in Extended Reality (XR) provides users with better awareness of where virtual elements are placed, and efficiently guides them to events such as notifications, system alerts from different windows, or approaching avatars. Humans, however, are inaccurate in localizing sound cues, especially with multiple sources due to limitations in human auditory perception such as angular discrimination error and front-back confusion. This decreases the efficiency of XR interfaces because users misidentify from which XR element a sound is coming. To address this, we propose Auptimize, a novel computational approach for placing XR sound sources, which mitigates such localization errors by utilizing the ventriloquist effect. Auptimize disentangles the sound source locations from the visual elements and relocates the sound sources to optimal positions for unambiguous identification of sound cues, avoiding errors due to inter-source proximity and front-back confusion. Our evaluation shows that Auptimize decreases spatial audio-based source identification errors compared to playing sound cues at the paired visual-sound locations. We demonstrate the applicability of Auptimize for diverse spatial audio-based interactive XR scenarios.2024HCHyunsung Cho et al.Social & Collaborative VRImmersion & Presence ResearchUIST
SonoHaptics: An Audio-Haptic Cursor for Gaze-Based Object Selection in XRWe introduce SonoHaptics, an audio-haptic cursor for gaze-based 3D object selection. SonoHaptics addresses challenges around providing accurate visual feedback during gaze-based selection in Extended Reality (XR), e.g., lack of world-locked displays in no- or limited-display smart glasses and visual inconsistencies. To enable users to distinguish objects without visual feedback, SonoHaptics employs the concept of cross-modal correspondence in human perception to map visual features of objects (color, size, position, material) to audio-haptic properties (pitch, amplitude, direction, timbre). We contribute data-driven models for determining cross-modal mappings of visual features to audio and haptic features, and a computational approach to automatically generate audio-haptic feedback for objects in the user's environment. SonoHaptics provides global feedback that is unique to each object in the scene, and local feedback to amplify differences between nearby objects. Our comparative evaluation shows that SonoHaptics enables accurate object identification and selection in a cluttered scene without visual feedback.2024HCHyunsung Cho et al.Mid-Air Haptics (Ultrasonic)Eye Tracking & Gaze InteractionSocial & Collaborative VRUIST
Predicting the Noticeability of Dynamic Virtual Elements in Virtual RealityWhile Virtual Reality (VR) systems can present virtual elements such as notifications anywhere, designing them so they are not missed by or distracting to users is highly challenging for content creators. To address this challenge, we introduce a novel approach to predict the noticeability of virtual elements. It computes the visual saliency distribution of what users see, and analyzes the temporal changes of the distribution with respect to the dynamic virtual elements that are animated. The computed features serve as input for a long short-term memory (LSTM) model that predicts whether a virtual element will be noticed. Our approach is based on data collected from 24 users in different VR environments performing tasks such as watching a video or typing. We evaluate our approach (n = 12), and show that it can predict the timing of when users notice a change to a virtual element within 2.56 sec compared to a ground truth, and demonstrate the versatility of our approach with a set of applications. We believe that our predictive approach opens the path for computational design tools that assist VR content creators in creating interfaces that automatically adapt virtual elements based on noticeability.2024ZLZhipeng Li et al.Carnegie Mellon University, Department of Computer Science and Technology, Tsinghua UniversityImmersion & Presence ResearchHuman-LLM CollaborationCHI
MineXR: Mining Personalized Extended Reality InterfacesExtended Reality (XR) interfaces offer engaging user experiences, but their effective design requires a nuanced understanding of user behavior and preferences. This knowledge is challenging to obtain without the widespread adoption of XR devices. We introduce MineXR, a design mining workflow and data analysis platform for collecting and analyzing personalized XR user interaction and experience data. MineXR enables elicitation of personalized interfaces from participants of a data collection: for any particular context, participants create interface elements using application screenshots from their own smartphone, place them in the environment, and simultaneously preview the resulting XR layout on a headset. Using MineXR, we contribute a dataset of personalized XR interfaces collected from 31 participants, consisting of 695 XR widgets created from 178 unique applications. We provide insights for XR widget functionalities, categories, clusters, UI element types, and placement. Our open-source tools and data support researchers and designers in developing future XR interfaces.2024HCHyunsung Cho et al.Carnegie Mellon UniversityMixed Reality WorkspacesImmersion & Presence ResearchInteractive Data VisualizationCHI
MARingBA: Music-Adaptive Ringtones for Blended Audio Notification DeliveryAudio notifications provide users with an efficient way to access information beyond their current focus of attention. Current notification delivery methods, like phone ringtones, are primarily optimized for high noticeability, enhancing situational awareness in some scenarios but causing disruption and annoyance in others. In this work, we build on the observation that music listening is now a commonplace practice and present MARingBA, a novel approach that blends ringtones into background music to modulate their noticeability. We contribute a design space exploration of music-adaptive manipulation parameters, including beat matching, key matching, and timbre modifications, to tailor ringtones to different songs. Through two studies, we demonstrate that MARingBA supports content creators in authoring audio notifications that fit low, medium, and high levels of urgency and noticeability. Additionally, end users prefer music-adaptive audio notifications over conventional delivery methods, such as volume fading.2024AWAlexander Wang et al.Carnegie Mellon UniversityCreative Collaboration & Feedback SystemsNotification & Interruption ManagementCHI
RealityReplay: Detecting and Replaying Temporal Changes In Situ Using Mixed Reality"Humans easily miss events in their surroundings due to limited short-term memory and field of view. This happens, for example, while watching an instructor's machine repair demonstration or conversing during a sports game. We present RealityReplay, a novel Mixed Reality (MR) approach that tracks and visualizes these significant events using in-situ MR visualizations without modifying the physical space. It requires only a head-mounted MR display and a 360-degree camera. We contribute a method for egocentric tracking of important motion events in users' surroundings based on a combination of semantic segmentation and saliency prediction, and generating in-situ MR visual summaries of temporal changes. These summary visualizations are overlaid onto the physical world to reveal which objects moved, in what order, and their trajectory, enabling users to observe previously hidden events. The visualizations are informed by a formative study comparing different styles on their effects on users' perception of temporal changes. Our evaluation shows that RealityReplay significantly enhances sensemaking of temporal motion events compared to memory-based recall. We demonstrate application scenarios in guidance, education, and observation, and discuss implications for extending human spatiotemporal capabilities through technological augmentation." https://doi.org/10.1145/36108882023HCHyunsung Cho et al.AR Navigation & Context AwarenessMixed Reality WorkspacesImmersion & Presence ResearchUbiComp
Parametric Haptics: Versatile Geometry-based Tactile Feedback DevicesHaptic feedback is important for immersive, assistive, or multimodal interfaces, but engineering devices that generalize across applications is notoriously difficult. To address the issue of versatility, we propose Parametric Haptics, geometry-based tactile feedback devices that are customizable to render a variety of tactile sensations. To achieve this, we integrate the actuation mechanism with the tactor geometry into passive 3D printable patches, which are then connected to a generic wearable actuation interface consisting of micro gear motors. The key benefit of our approach is that the 3D-printed patches are modular, can consist of varying numbers and shapes of tactors, and that the tactors can be grouped and moved by our actuation geometry over large areas of the skin. The patches are soft, thin, conformable, and easy to customize to different use cases, thus potentially enabling a large design space of diverse tactile sensations. In our user study, we investigate the mapping between geometry parameters of our haptic patches and users’ tactile perceptions. Results indicate a good agreement between our parameters and the reported sensations, showing initial evidence that our haptic patches can produce a wide range of sensations for diverse use scenarios. We demonstrate the utility of our approach with wearable prototypes in immersive Virtual Reality (VR) scenarios, embedded into wearable objects such as glasses, and as wearable navigation and notification interfaces. We support designing such patches with a design tool in Rhino.2023VHViolet Yinuo Han et al.Haptic WearablesShape-Changing Interfaces & Soft Robotic MaterialsUIST
HandAvatar: Embodying Non-Humanoid Virtual Avatars through HandsWe propose HandAvatar to enable users to embody non-humanoid avatars using their hands. HandAvatar leverages the high dexterity and coordination of users' hands to control virtual avatars, enabled through our novel approach for automatically-generated joint-to-joint mappings. We contribute an observation study to understand users’ preferences on hand-to-avatar mappings on eight avatars. Leveraging insights from the study, we present an automated approach that generates mappings between users' hands and arbitrary virtual avatars by jointly optimizing control precision, structural similarity, and comfort. We evaluated HandAvatar on static posing, dynamic animation, and creative exploration tasks. Results indicate that HandAvatar enables more precise control, requires less physical effort, and brings comparable embodiment compared to a state-of-the-art body-to-avatar control method. We demonstrate HandAvatar's potential with applications including non-humanoid avatar based social interaction in VR, 3D animation composition, and VR scene design with physical proxies. We believe that HandAvatar unlocks new interaction opportunities, especially for usage in Virtual Reality, by letting users become the avatar in applications including virtual social interaction, animation, gaming, or education.2023YJYu Jiang et al.Tsinghua UniversityHand Gesture RecognitionMixed Reality WorkspacesIdentity & Avatars in XRCHI
Towards Understanding Diminished RealityDiminished reality (DR) refers to the concept of removing content from a user's visual environment. While its implementation is becoming feasible, it is still unclear how users perceive and interact in DR-enabled environments and what applications it benefits. To address this challenge, we first conduct a formative study to compare user perceptions of DR and mediated reality effects (e.g., changing the color or size of target elements) in four example scenarios. Participants preferred removing objects through opacity reduction (i.e., the standard DR implementation) and appreciated mechanisms for maintaining a contextual understanding of diminished items (e.g., outlining). In a second study, we explore the user experience of performing tasks within DR-enabled environments. Participants selected which objects to diminish and the magnitude of the effects when performing two separate tasks (video viewing, assembly). Participants were comfortable with decreased contextual understanding, particularly for less mobile tasks. Based on the results, we define guidelines for creating general DR-enabled environments.2022YCYifei Cheng et al.Swarthmore CollegeMixed Reality WorkspacesImmersion & Presence ResearchContext-Aware ComputingCHI
SemanticAdapt: Optimization-based Adaptation of Mixed Reality Layouts Leveraging Virtual-Physical Semantic ConnectionsWe present an optimization-based approach that automatically adapts Mixed Reality (MR) interfaces to different physical environments. Current MR layouts, including the position and scale of virtual interface elements, need to be manually adapted by users whenever they move between environments, and whenever they switch tasks. This process is tedious and time consuming, and arguably needs to be automated for MR systems to be beneficial for end users. We contribute an approach that formulates this challenge as a combinatorial optimization problem and automatically decides the placement of virtual interface elements in new environments. To achieve this, we exploit the semantic association between the virtual interface elements and physical objects in an environment. Our optimization furthermore considers the utility of elements for users' current task, layout factors, and spatio-temporal consistency to previous layouts. All those factors are combined in a single linear program, which is used to adapt the layout of MR interfaces in real time. We demonstrate a set of application scenarios, showcasing the versatility and applicability of our approach. Finally, we show that compared to a naive adaptive baseline approach that does not take semantic associations into account, our approach decreased the number of manual interface adaptations by 33\%.2021YCYifei Cheng et al.AR Navigation & Context AwarenessMixed Reality WorkspacesContext-Aware ComputingUIST