Papers & Patents
Publications & Patents
256 outputs
256 outputs
202618
Proceedings of the 2026 CHI Conference on Human Factors in Computing SystemsTop-tier
Question-asking is one of the key indicators of cognitive engagement. However, understanding how the distinct psychological affordances of presentation media shape learners’ spoken inquiries with embodied Intelligent Virtual Agents (IVAs) remains limited. To systematically examine this process, we propose a 5W1H-based framework for analyzing learner questions. Using this framework, we conducted a user study comparing an Augmented Reality-based IVA (AR-IVA) deployed in the physical environment with a screen-based IVA (Video-IVA) during cardiopulmonary resuscitation (CPR) instruction. Results showed that the AR-IVA elicited higher spatial and social presence and promoted more frequent and longer questions focused on clarification and understanding. In contrast, the Video-IVA encouraged questions regarding procedural refinement. Presence acted as a selective filter, shaping the timing and …
IEEE Conference on Virtual Reality and 3D User Interfaces (VR) 2026Top-tier
We propose a novel object-mediated hand interaction system that enables real-time operation with everyday objects on wearable augmented reality (AR) devices. Despite recent advances, both commercial and academic hand interaction techniques remain constrained, typically requiring external hardware or depending exclusively on bare-hand gestures. Motivated by these constraints, we developed an offloading framework that integrates a high-fidelity transformer-based 3D hand reconstruction model with a dynamic gesture recognition network powered by gated recurrent units (GRU). This architecture ensures stable and accurate gesture recognition even during interaction with physical objects. To evaluate its quantitative performance, we collected a custom dataset based on a predefined gesture set, achieving 93.0% accuracy in 5-fold cross-validation. The complete system implemented on Microsoft HoloLens 2 operates at a real-time framerate, and we further analyze the latency of each step in our framework. Through this interaction paradigm, users can experience immersive and intuitive AR in everyday environments with minimal disruption to natural action behavior. Our projects are available at https://github.com/kaist-uvrlab/UnifiedHOInteraction.
IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) 2026
We present a VR research demo for practical telepresence with photorealistic 3D Gaussian Splatting (3DGS) avatars under HMD-only sensing. The demo consists of two parts that provide complementary experiences in a single session: a face part, where HMD-provided blendshape signals drive an expressive 3DGS head avatar rendered stereoscopically on the client, and a full-body part, where a user-specific 3DGS full-body avatar is created from a single image and driven using HMD signals. During the demo, participants first capture a single image for full-body avatar creation, experience facial control using a pre-built third-person head avatar while the full-body avatar is generated, and then control their personalized full-body avatar in VR. By enabling both facial and full-body controllability and contrasting two rendering-responsibility designs, client-side direct 3D rendering and server-side stereoscopic image rendering/streaming-the demo offers practical insights for designing responsive, realistic VR telepresence systems under HMD-only sensing.
IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) 2026
We present a real-time AR system that enables interaction with physical cuboid- and cylinder-shaped objects by representing them as compact superquadrics. Our approach uses Instant Surface Normal (ISN) maps-color images encoding per-pixel geometry-to detect and segment objects based on shape rather than appearance. Superquadric parameters are extracted sequentially, allowing efficient and updatable 3D modeling in real time. Experimental results show accurate detection, consistent segmentation, and reconstruction quality comparable to existing methods, while achieving over 5× faster inference. This system allows physical objects to be seamlessly integrated as interactive elements in dynamic AR environments.
IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) 2026
We propose VR Zen Garden, a virtual reality (VR) application designed to support stress relief and meditation through interactive spatial soundscapes. The system enables users to create personalized auditory environments by combining two key interactions: Object Placement, which spatializes sound-emitting objects, and Sand Pattern Raking, which modulates sound properties through visual patterns. These inputs are processed by the Sound Spatializer and the Metaphoric Sound Mixer, allowing users to intuitively shape immersive 3D soundscapes in real time. VR Zen Garden offers an engaging and embodied approach to mindfulness, highlighting the potential of sound-driven design in immersive wellness applications.
Other · Granted
An edge-centric space rescaling method which is a new space rescaling technique for registering dissimilar physical-virtual spaces by utilizing the effects of adjusting physical space with redirected walking (RDW) and a system therefor are provided. The edge-centric space rescaling method includes dividing physical space into grid units based on basic geometric information of an object, applying relative translation gains (RTGs) to physical space grids divided in redirected walking (RDW), and registering objects of interest in virtual space to a corresponding target in physical space to rescale the virtual space to the physical space, while maintaining a coordinate system between the virtual
IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VR 2026)Top 10%
We propose event-based vibrotactile feedback as an effective approach for enhancing bare-hand XR interaction, delivered through commodity smartwatches that users already wear. While controllers and haptic gloves provide rich tactile feedback, they introduce additional hardware that users must carry and wear throughout the day. Importantly, they interpose hardware between the user and the virtual world, occupying the hands, constraining finger motion, and undermining truly unobstructed bare-hand interaction. Our proposed approach delivers short discrete pulses at key manipulation moments such as object contact and state changes, providing tactile confirmation at the wrist without requiring additional hardware or disrupting finger movement. Two user studies with 26 participants demonstrate that this referred feedback significantly enhances user experience at either wrist placement, with 94% finding it helpful and particular benefits during complex tasks like knob rotation where haptic cues reduced visual attention demands. These findings establish design guidelines for integrating accessible haptics into everyday XR through personal devices, supporting broader adoption of natural hand interaction.
IEEE Transactions on Visualization and Computer GraphicsTop 10%
We present ForceCtrl, a novel 3D hand raycasting technique that enhances pointing precision based on control-display (CD) gain controlled with user-defined pinch force. We introduce a target-agnostic approach for refining raycasting precision, overcoming limitations in human motor accuracy. User-defined pinch force, detected with surface electromyography (sEMG), enables users to easily activate or deactivate CD gain during interaction. We propose three CD gain strategies and compare them through target selection and placement tasks. Our system reduces selection errors, placement jitters, and user workload, especially for distant targets in high-difficulty tasks. These results highlight the effectiveness of applying CD gain to hand raycasting and demonstrate the potential of user-defined pinch force as a robust input modality for precise hand interaction in AR/VR.
Other · Granted
The present invention relates to a hand position estimation method and system for estimating a 3-dimensional hand position for quick hand movement, on the basis of domain transfer learning from a depth image to an infrared image, the hand position estimation method comprising the steps of: processing a depth image and an infrared image for hand movement; synthesizing a depth map with the infrared image by using a hand image generator (HIG), and estimating skeletal positions of hand joints from each of the depth map and an infrared map; and calculating a 3-dimensional hand position by using the skeletal positions and the center of a hand depth image.
IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VR 2026)Top 10%
We propose Int3DNet, a scene-aware network that predicts 3D intention areas directly from scene geometry and head-hand motion cues, enabling robust human intention prediction without explicit object-level perception. In Mixed Reality (MR), intention prediction is critical as it enables the system to anticipate user actions and respond proactively, reducing interaction delays and ensuring seamless user experiences. Our method employs a cross attention fusion of sparse motion cues and scene point clouds, offering a novel approach that directly interprets the user's spatial intention within the scene. We evaluated Int3DNet on MoGaze and CIRCLE datasets, which are public datasets for full-body human-scene interactions, showing consistent performance across time horizons of up to 1500 ms and outperforming the baselines, even in diverse and unseen scenes. Moreover, we demonstrate the usability of proposed method through a demonstration of efficient visual question answering (VQA) based on intention areas. Int3DNet provides reliable 3D intention areas derived from head-hand motion and scene geometry, thus enabling seamless interaction between humans and MR systems through proactive processing of intention areas.
IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VR 2026)Top 10%
We propose OFERA, a novel framework for real-time expression control of photorealistic Gaussian head avatars for VR headset users. Existing approaches attempt to recover occluded facial expressions using additional sensors or internal cameras, but sensor-based methods increase device weight and discomfort, while camera-based methods raise privacy concerns and suffer from limited access to raw data. To overcome these limitations, we leverage the blendshape signals provided by commercial VR headsets as expression inputs. Our framework consists of three key components: (1) Blendshape Distribution Alignment (BDA), which applies linear regression to align the headset-provided blendshape distribution to a canonical input space; (2) an Expression Parameter Mapper (EPM) that maps the aligned blendshape signals into an expression parameter space for controlling Gaussian head avatars; and (3) a Mapper-integrated Avatar (MiA) that incorporates EPM into the avatar learning process to ensure distributional consistency. Furthermore, OFERA establishes an end-to-end pipeline that senses and maps expressions, updates Gaussian avatars, and renders them in real-time within VR environments. We show that EPM outperforms existing mapping methods on quantitative metrics, and we demonstrate through a user study that the full OFERA framework enhances expression fidelity while preserving avatar realism. By enabling real-time and photorealistic avatar expression control, OFERA significantly improves telepresence in VR communication. A project page is available at https://ysshwan147.github.io/projects/ofera/.
IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VR 2026)Top 10%
We introduce SceneLinker, a novel framework that generates compositional 3D scenes via semantic scene graph from RGB sequences. To adaptively experience Mixed Reality (MR) content based on each user's space, it is essential to generate a 3D scene that reflects the real-world layout by compactly capturing the semantic cues of the surroundings. Prior works struggled to fully capture the contextual relationship between objects or mainly focused on synthesizing diverse shapes, making it challenging to generate 3D scenes aligned with object arrangements. We address these challenges by designing a graph network with cross-check feature attention for scene graph prediction and constructing a graph-variational autoencoder (graph-VAE), which consists of a joint shape and layout block for 3D scene generation. Experiments on the 3RScan/3DSSG and SG-FRONT datasets demonstrate that our approach outperforms state-of-the-art methods in both quantitative and qualitative evaluations, even in complex indoor environments and under challenging scene graph constraints. Our work enables users to generate consistent 3D spaces from their physical environments via scene graphs, allowing them to create spatial MR content. Project page is https://scenelinker2026.github.io.
IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VR 2026)Top 10%
We propose a spatial affordance-aware affine transformation method between heterogeneous spaces for continuous multi-object matching in shared Mixed Reality (MR) spaces. While previous redirection and spatial mapping approaches utilize physical objects and walkable areas, a critical gap remains in enabling continuous mapping between dissimilar physical environments that supports both precise object alignment and seamless locomotion in a shared space. Our method structurally segments heterogeneous spaces into interaction zones and constructs affine patches based on object adjacency and facing configuration, enabling continuous correspondence. We evaluate our method using a dataset of paired dissimilar spaces and demonstrate that, unlike conventional grid-based methods, our approach achieves broader spatial alignment and richer object matching. The results show that our method can serve as an effective mapping framework for shared environments requiring semantic continuity and structural coherence across diverse real-world spaces.
IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VR 2026)Top 10%
This study explores a streamlined facial data collection method for conversational contexts, addressing the limitations of existing approaches that often require extensive datasets and prioritize technical metrics over user perception and experience. We systematically investigate which facial expression data are essential for reconstructing photorealistic avatars and how they can be captured efficiently. Our research employs a two-phase methodology to identify efficient facial data collection strategies and evaluate their effectiveness. In the first phase, we conduct facial data acquisition and evaluate reconstruction performance using utterance data and emotional data. In the second phase, we carry out a comprehensive user evaluation comparing three progressive conditions: utterance only, utterance and emotional data, and a control condition involving extensive data. Findings from 24 participants engaged in simulated face-to-face conversations reveal that targeted utterance and emotional data achieve comparable levels of perceived realism, naturalness, and telepresence, while reducing training time and data usage when compared to the extensive data collection approach. These results demonstrate that targeted data inputs can enable efficient avatar face reconstruction, offering practical guidelines for real-time applications such as AR/VR telepresence and highlighting the trade-off between data quantity and perceived quality.
IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VR'26)Top 10%
We propose a method for generating task breakpoints based on an Origin-Centric Graph (OCG) to segment goal-oriented activity recordings into task units for adaptive playback in Virtual Reality (VR) environments. With the development of Augmented Reality (AR)/VR head-mounted displays (HMDs), research on adaptive tutorials and authoring tools has become active, but existing task segmentation methods mainly rely on manual annotation or are restricted to 2D video which limits their applicability to 3D VR contexts. In our approach, assembly scenarios with clearly defined task boundaries are recorded using a structured spatio-temporal scene graph (STSG), and the OCG is employed to track changes in the central object and the formation of new groups, thereby generating task breakpoints automatically. A user study collected user-perceived task breakpoints to establish ground truth (GT), and comparison with the algorithm-detected breakpoints demonstrated high agreement and confirmed accuracy in supporting adaptive playback. The proposed task segmentation method provides a foundation for dynamically adjusting VR playback according to user proficiency and progress, with potential for extension into automatic timeline segmentation systems for diverse VR recordings.
IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VR'26)Top 10%
We present VRGaussianAvatar, an integrated system that enables real-time full-body 3D Gaussian Splatting (3DGS) avatars in virtual reality using only head-mounted display (HMD) tracking signals. The system adopts a parallel pipeline with a VR Frontend and a GA Backend. The VR Frontend uses inverse kinematics to estimate full-body pose and streams the resulting pose along with stereo camera parameters to the backend. The GA Backend stereoscopically renders a 3DGS avatar reconstructed from a single image. To improve stereo rendering efficiency, we introduce Binocular Batching, which jointly processes left and right eye views in a single batched pass to reduce redundant computation and support high-resolution VR displays. We evaluate VRGaussianAvatar with quantitative performance tests and a within-subject user study against image- and video-based mesh avatar baselines. Results show that VRGaussianAvatar sustains interactive VR performance and yields higher perceived appearance similarity, embodiment, and plausibility. Project page and source code are available at https://vrgaussianavatar.github.io.
HCI Korea 2026
KR 10-2924674-0000 · Korea · Granted
본 발명은, 이동 단말기의 전면에 입력을 가하는 펜 및, 사용자에게 증강현실의 3 차원 공간을 제공하는 증강현실 장치를 구비하고, 증강현실 장치는, 이동 단말기 및 펜과 관련된 정보를 디지털 데이터로 전환하여 3 차원 공간과 연결하고, 3 차원 공간에 3 차원 오브젝트를 출력하며, 이동 단말기의 전면을 3 차원 오브젝트와 연계하여 제어명령을 가하는 2 차원 받침면으로 설정하고, 2 차원 받침면에서 이동하는 펜의 움직임을 모니터링하여 제어명령을 생성하는, 증강현실 데스크톱 시스템을 제공한다.
202538
SIGGRAPH Asia 2025 XR
We propose a collaborative scene mood authoring system in a virtual reality (VR) environment, where multiple users design and control the multimodal feedback (i.e., audiovisual and haptic) of a shared scene using voice commands. To support this, the user’s command is processed by large language models (LLMs) with the scene metadata and mood presets to identify intended target objects and mood, and the system modifies the objects to match the desired atmospheric expression. To cover a wide range of affective moods in scene mood authoring, we devised four distinct mood presets that represent different combinations of arousal and valence across the affective space. Each preset is expressed through a set of labeled visual, auditory, and haptic feedback elements, enabling coherent and multimodal expression of affective states within the scene.
IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) 2025
We present a spatial augmentation system designed for Augmented Reality (AR) exhibition experiences, focusing on the immersive reinterpretation of two-dimensional artworks. Existing AR exhibition applications primarily augment content related to the artwork, but fall short of its architectural elements, which are crucial for integrating the artwork into the viewer’s surrounding space and enabling immersive spatial alignment. This study proposes a spatial augmentation approach that takes an artwork image as input, generates floor patterns and architectural 3D models through 2D and 3D model generation pipelines, and semantically aligns and augments them in the physical space. This spatial augmentation intends to enhance viewer engagement and perceived presence by maintaining spatial coherence between virtual and real space. By activating architectural surfaces as part of the experience, the system repositions AR exhibitions as spatial narratives rather than conventional overlay-driven displays.
IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) 2025
We propose a lifestyle quantification method for personalized augmented reality (AR) intervention in everyday life. Maintaining a healthy lifestyle accompanies extensive workforce and costs, because accurate health evaluation and tailored management is a prerequisite. However, research on assessing multiple dimensions of health and timely administration of personalized information has been lacking. To address these issues, first we deduced participant’s physical activity, nutrition, interpersonal relations, and stress management level through their mobile devices. Each participant’s week-long data were filtered by clinical features evidenced in high-level medical literature. Next, based on their health status, we presented personalized AR interventions in their simulated everyday environment. Statistical analysis and user experiment results verified the clinical capability of our method and its effect on health-promoting decision behaviors of participants. Through this feasibility study, we hope to contribute on tailored lifestyle management in our daily lives, for better health of all.
IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) 2025
We present a fast and efficient method for transferring facial textures onto SMPL-X–based full-body avatars. Unlike conventional affine-transform methods that are slow and prone to visual artifacts, our method utilizes a barycentric UV conversion technique. Our approach precomputes the entire UV mapping into a single transformation matrix, enabling texture transfer in a single operation. This results in a speedup of over 7000x compared to the baseline, while also significantly improving the final texture quality by eliminating boundary artifacts. Through quantitative and qualitative evaluations, we demonstrate that our method offers a practical solution for personalization in immersive XR applications. The code is available online.1
IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) 2025
We present a multimodal framework that uses few-shot language models to generate action scene graphs from raw video for structuring spatial experiences in extended reality (XR). The system integrates unsupervised action segmentation, multimodal captioning with a vision-language model (VLM) guided by speech and motion cues, and scene graph generation from captions using a large language model (LLM), both leveraging few-shot prompting. By combining visual, verbal, and motion inputs, the framework produces semantically rich and accurate representations of spatial experiences without requiring annotated data or model fine-tuning. The resulting outputs will support a range of downstream XR applications, including adaptive playback, intent-driven retrieval, and flexible content reconstruction.
IEEE International Symposium on Mixed and Augmented Reality (ISMAR) 2025Top-tier
As immersive virtual environments become more prevalent, avatars serve as critical social interfaces. This study explores how combinations of visual appearance, vocal characteristics, and informed identity influence users' initial impressions and partner preferences in four distinct XR scenarios: physical, intellectual, social, and romantic. A within-subject experiment with 40 participants assessed perceived warmth, competence, uncanniness, and selection preferences across diverse avatar configurations. Results indicate that vocal cues had a particularly strong impact on social perception, often shaping feelings of approachability and clarity in communication. While some cue alignments enhanced perceived social comfort and engagement, inconsistencies across gender-related cues occasionally led to increased perceptions of uncanniness, especially in emotionally sensitive contexts. These findings highlight the importance of designing avatars that thoughtfully adapt to different interaction contexts, supporting inclusive and responsive user experiences in social XR platforms.