arXiv subjects

Sang Ho Yoon

Publications and source records attributed to Sang Ho Yoon.

At least 19 recordsLinked to original sources

arXiv2026

PlanePivoting: Exploration and Optimization of Gaze-Mouse Cursor Alignment for Spatial Object Translation

As XR matures into a ubiquitous computing platform, the disconnect between 2D and 3D input modalities remains a critical barrier to seamless workflow. Frequent transitions between the mouse for 2D precision and hand gestures for 3D manipulation induce significant physical fatigue and cognitive load. To address this, we introduce PlanePivoting, a multimodal interaction technique that extends standard mouse input into 3D space by leveraging gaze-mouse alignment. This technique dynamically modulates the translation plane based on the spatial overlap between the gaze and mouse cursor, eliminating the need for physical input modality switching. To systematically explore the foundational design space of gaze-mouse coordination and optimize key variables, we conducted a user study comparing PlanePivoting with a standard 3D Gizmo interface across two translation mapping profiles and two gaze cursor apertures. Results demonstrate that PlanePivoting outperforms the Gizmo on efficiency metrics while maintaining comparable precision and yielding higher subjective satisfaction. This study demonstrates the potential of gaze-mouse alignment for efficient spatial manipulation between 2D and 3D environments.

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arXiv2026

DOBI: Dynamic Opportunistic Body Input via Spare Joint Recruitment for Hands-Free XR

Extended Reality (XR) systems are often most useful when users are engaged in ongoing physical tasks, yet current interaction techniques still largely assume the hands are available. We present opportunistic body input, an interaction paradigm that redirects continuous XR control to whichever available body region remains free in the moment. To investigate how users naturally coordinate these spare-body movements, we conducted an elicitation study across six hand-busy scenarios. We found that while users' preferred spare body regions shift dynamically based on physical constraints, the resulting spontaneous movements share a consistent, low-dimensional kinematic structure organized around a dominant principal axis. Building on these findings, we present DOBI (Dynamic Opportunistic Body Input), a real-time XR technique that uses gaze to target a UI element, a brief trigger gesture to identify the recruited spare body region, and the region's subsequent motion to drive continuous 1D control. A 1D Fitts' law study establishes the baseline motor performance of this paradigm across four distinct body regions, achieving throughputs up to 2.62 bits/s with an overall 5.0% error rate, and a dual-task usability study shows that DOBI supports reliable, low-effort control (SUS = 84.2) while users remain engaged in realistic hand-busy activities.

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arXiv2026

UniScale: Exploring Unimanual Gesture Mapping Strategies for Gaze+Pinch-based Scaling Interaction

Object scaling serves as a fundamental spatial manipulation that enables complex and productive tasks in XR environments. This paper investigates unimanual scaling techniques for XR using gaze and hand interactions. We propose UniScale, a set of unimanual alternatives to the standard bimanual pinch, allowing users to scale objects while preserving hand availability for concurrent spatial manipulations. We design five distinct mapping strategies based on physical metaphors, exploring unimanual control that varies depth, angle, micro-gestures, and finger-distance input. We then compare these techniques against a standard bimanual baseline, in which users adjust the inter-hand distance via a bimanual pinch gesture. In a user study, we evaluate their effectiveness in a 3D object scaling task under both clutching and clutching-free conditions. The results indicate that while bimanual scaling relies on clutching for stable control, unimanual techniques excel in clutching-free conditions, significantly reducing physical hand movement. From the results, we derive valuable design implications for developing efficient 3D multimodal interactions in XR.

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arXiv2026

GazeTune: Facilitating Precise Gaze-Driven Interactions with Cascaded Touch Input

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

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arXiv2026

Sharing Roughness with Hand-Outline Visualization to Reduce Sensory Asymmetry in VR Collaboration

In collaborative VR, asymmetric access to haptic hardware creates a critical information gap: tactile evidence remains private to the haptic user, hindering the shared understanding needed for joint decision-making. While prior work has explored crossmodal sensory cues in virtual environments, it remains unclear how such cues should be designed for asymmetric collaboration, where collaborators receive information through different modalities. In our setting, the haptic user feels roughness through fingertip vibration, whereas the non-haptic user relies on vision alone. To reduce this asymmetry, we propose externalizing an object's tactile state through a glanceable hand-outline visual proxy. Specifically, we examine whether abstract visual roughness cues based on line shape and motion can encode three discrete roughness levels for both haptic and non-haptic users. Two preliminary studies establish a shared visual semantics by identifying visually distinguishable cues for non-haptic users and validating their visuo-haptic correspondence for haptic users. In a main study of a collaborative sorting task, showing this visualization on both users' hands significantly reduced completion time relative to a no-visualization baseline. Moreover, NU-side cue visibility was associated with higher confidence and perceived contribution for the non-haptic user. These findings show that hand-anchored abstract visual cues provide a lightweight means of externalizing object tactile state, reducing information asymmetry without compromising social presence.

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arXiv2026

Understanding Organizational Strategies Across Multimodal Artifacts in Immersive Computational Notebooks

Immersive Computational Notebooks (ICoN) extend traditional notebook environments into immersive spaces, enabling analysts to interact with multimodal artifacts, including code, narratives, data tables, and visualizations. By integrating multimodal artifacts into a single immersive workspace, ICoN enables analysts to transition between analytical tasks seamlessly. Meanwhile, understanding organizational strategies is critical for designing effective interactions to further support analysts. However, prior research on immersive computational notebooks has primarily examined organizational strategies centered on single-modality artifacts. Systematic investigations of how analysts spatially organize the complex relationships among multimodal artifacts in a single immersive workspace remain underexplored. To address this gap, we conducted a user study to examine organizational strategies for multimodal artifacts in immersive computational notebooks. Our findings show that participants predominantly adopted depth-based layouts, and their spatial organization was largely structured around cell-based artifacts.

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arXiv2026

GuardMarkGS: Unified Ownership Tracing and Edit Deterrence for 3D Gaussian Splatting

3D Gaussian Splatting (3DGS) is becoming a practical representation for novel view synthesis, but its growing adoption, together with rapid advances in instruction-driven 3DGS editing, also exposes a dual copyright risk: once a 3DGS-based asset is released, it can be used without permission and manipulated through 3D editing. Existing protection methods address only one side of this problem. Watermarking can trace ownership after unauthorized use, but it cannot prevent malicious editing. Adversarial edit-deterrence methods can disrupt editing, but they do not provide evidence of ownership. To the best of our knowledge, we present the first unified protection framework for 3DGS that jointly optimizes ownership tracing and unauthorized editing deterrence. Our framework combines a scene-wide watermarking objective over all Gaussians with an adversarial objective for edit deterrence. The adversarial branch combines latent-anchor separation, denoising-trajectory diversion, and cross-attention diversion to divert the editing trajectory, while an update-saliency-motivated Gaussian selection strategy assigns stronger adversarial updates to mask-selected Gaussians, improving the balance among watermark recovery, edit deterrence, and rendering fidelity. Experiments on scenes from Mip-NeRF 360 and Instruct-NeRF2NeRF demonstrate that the proposed framework achieves a favorable balance among bit accuracy, edit deterrence, and rendering quality. These results suggest that practical copyright protection of 3DGS-based assets can be more effectively addressed by integrating ownership tracing and unauthorized editing deterrence into a single optimization framework.

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arXiv2026

Make it Simple, Make it Dance: Dance Motion Simplification to Support Novices' Dance Learning

Online dance tutorials have gained widespread popularity. However, many novices encounter difficulties when dance motion complexity exceeds their skill level, potentially leading to discouragement. This study explores dance motion simplification to address this challenge. We surveyed 30 novices to identify challenging movements, then conducted focus groups with 30 professional choreographers across 10 genres to explore simplification strategies and collect paired original-simplified dance datasets. We identified five complexity factors and developed automated simplification methods using both rule-based and learning-based approaches. We validated our approach through three evaluations. Technical evaluation confirmed our complexity measures and algorithms. 20 professional choreographers assessed motion naturalness, simplification adequacy, and style preservation. 18 novices evaluated learning effectiveness through workload, self-efficacy, objective performance, and perceived difficulty. This work contributes to dance education technology by proposing methods that help make choreography more approachable for beginners while preserving essential characteristics.

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arXiv2026

Align-to-Scale: Mode Switching Technique for Unimanual 3D Object Manipulation with Gaze-Hand-Object Alignment in Extended Reality

As extended reality (XR) technologies rapidly become as ubiquitous as today's mobile devices, supporting one-handed interaction becomes essential for XR. However, the prevalent Gaze + Pinch interaction model partially supports unimanual interaction, where users select, move, and rotate objects with one hand, but scaling typically requires both hands. In this work, we leverage the spatial alignment between gaze and hand as a mode switch to enable single-handed pinch-to-scale. We design and evaluate several techniques geared for one-handed scaling and assess their usability in a compound translate-scale task. Our findings show that all proposed methods effectively enable one-handed scaling, but each method offers distinct advantages and trade-offs. To this end, we derive design guidelines to support futuristic 3D interfaces with unimanual interaction. Our work helps make eye-hand 3D interaction in XR more mobile, flexible, and accessible.

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arXiv2026

Finger Tendon Vibration: Finger Movement Illusions for Immersive Virtual Object Interaction

The absence of physical information during hand-object interaction in a virtual environment diminishes realism and immersion. Kinesthetic haptic feedback has proven effective in delivering realistic object-derived haptic cues, enhancing the overall virtual reality (VR) experience. Here, we propose kinesthetic illusion through a novel application of finger tendon vibration (FTV), which creates an illusory sensation of finger movement. To effectively apply FTV for virtual object interactions, we first examine the effects of short-duration FTV (<5 s) through 3 perception studies. Based on study results, we design 6 exemplary VR scenarios, representing the overall design space of VR object interactions, and 4 different haptic rendering strategies for FTV. We evaluated these rendering methods on each VR scenario and derived a design guideline for FTV application. We then compared FTV with no vibration and simple vibration, observing that FTV enhances VR experience by providing realistic resistance on the finger, greatly improving body ownership.

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arXiv2026

HOICraft: In-Situ VLM-based Authoring Tool for Part-Level Hand-Object Interaction Design in VR

Hand-Object Interaction (HOI) is a key interaction component in Virtual Reality (VR). However, designing HOI still requires manual efforts to decide how object should be selected and manipulated, while also considering user abilities, which leads to time-consuming refinements. We present HOICraft, a VLM-based in-situ HOI authoring tool that enables part-level interaction design in VR. Here, HOICraft assists designers by recommending interactable elements from 3D objects, customizing HOI design properties, and mapping hand movement with virtual object behavior. We conducted a formative study with three expert VR designers to identify five representative HOI designs to support diverse user experiences. Building upon preference data from 20 participants, we develop an HOI mapping module with in-context learning. In a user study with 12 VR interaction designers, HOI mapping from HOICraft significantly reduced trial-and-error iterations compared to manual authoring. Finally, we assessed the usability of HOICraft, demonstrating its effectiveness for HOI design in VR.

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arXiv2025

FaceShield: Defending Facial Image against Deepfake Threats

The rising use of deepfakes in criminal activities presents a significant issue, inciting widespread controversy. While numerous studies have tackled this problem, most primarily focus on deepfake detection. These reactive solutions are insufficient as a fundamental approach for crimes where authenticity is disregarded. Existing proactive defenses also have limitations, as they are effective only for deepfake models based on specific Generative Adversarial Networks (GANs), making them less applicable in light of recent advancements in diffusion-based models. In this paper, we propose a proactive defense method named FaceShield, which introduces novel defense strategies targeting deepfakes generated by Diffusion Models (DMs) and facilitates defenses on various existing GAN-based deepfake models through facial feature extractor manipulations. Our approach consists of three main components: (i) manipulating the attention mechanism of DMs to exclude protected facial features during the denoising process, (ii) targeting prominent facial feature extraction models to enhance the robustness of our adversarial perturbation, and (iii) employing Gaussian blur and low-pass filtering techniques to improve imperceptibility while enhancing robustness against JPEG compression. Experimental results on the CelebA-HQ and VGGFace2-HQ datasets demonstrate that our method achieves state-of-the-art performance against the latest deepfake models based on DMs, while also exhibiting transferability to GANs and showcasing greater imperceptibility of noise along with enhanced robustness. Code is available here: https://github.com/kuai-lab/iccv25_faceshield

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arXiv2025

Visuo-Tactile Feedback with Hand Outline Styles for Modulating Affective Roughness Perception

We propose a visuo-tactile feedback method that combines virtual hand visualization and fingertip vibrations to modulate affective roughness perception in VR. While prior work has focused on object-based textures and vibrotactile feedback, the role of visual feedback on virtual hands remains underexplored. Our approach introduces affective visual cues including line shape, motion, and color applied to hand outlines, and examines their influence on both affective responses (arousal, valence) and perceived roughness. Results show that sharp contours enhanced perceived roughness, increased arousal, and reduced valence, intensifying the emotional impact of haptic feedback. In contrast, color affected valence only, with red consistently lowering emotional positivity. These effects were especially noticeable at lower haptic intensities, where visual cues extended affective modulation into mid-level perceptual ranges. Overall, the findings highlight how integrating expressive visual cues with tactile feedback can enrich affective rendering and offer flexible emotional tuning in immersive VR interactions.

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arXiv2025

AfforDance: Personalized AR Dance Learning System with Visual Affordance

We propose AfforDance, an augmented reality (AR)-based dance learning system that generates personalized learning content and enhances learning through visual affordances. Our system converts user-selected dance videos into interactive learning experiences by integrating 3D reference avatars, audio synchronization, and adaptive visual cues that guide movement execution. This work contributes to personalized dance education by offering an adaptable, user-centered learning interface.

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arXiv2025

VibWalk: Mapping Lower-limb Haptic Experiences of Everyday Walking

Walking is among the most common human activities where the feet can gather rich tactile information from the ground. The dynamic contact between the feet and the ground generates vibration signals that can be sensed by the foot skin. While existing research focuses on foot pressure sensing and lower-limb interactions, methods of decoding tactile information from foot vibrations remain underexplored. Here, we propose a foot-equipped wearable system capable of recording wideband vibration signals during walking activities. By enabling location-based recording, our system generates maps of haptic data that encode information on ground materials, lower-limb activities, and road conditions. Its efficacy was demonstrated through studies involving 31 users walking over 18 different ground textures, achieving an overall identification accuracy exceeding 95\% (cross-user accuracy of 87\%). Our system allows pedestrians to map haptic information through their daily walking activities, which has potential applications in creating digitalized walking experiences and monitoring road conditions.

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arXiv2024

FPANet: Frequency-based Video Demoireing using Frame-level Post Alignment

Moire patterns, created by the interference between overlapping grid patterns in the pixel space, degrade the visual quality of images and videos. Therefore, removing such patterns~(demoireing) is crucial, yet remains a challenge due to their complexities in sizes and distortions. Conventional methods mainly tackle this task by only exploiting the spatial domain of the input images, limiting their capabilities in removing large-scale moire patterns. Therefore, this work proposes FPANet, an image-video demoireing network that learns filters in both frequency and spatial domains, improving the restoration quality by removing various sizes of moire patterns. To further enhance, our model takes multiple consecutive frames, learning to extract frame-invariant content features and outputting better quality temporally consistent images. We demonstrate the effectiveness of our proposed method with a publicly available large-scale dataset, observing that ours outperforms the state-of-the-art approaches in terms of image and video quality metrics and visual experience.

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arXiv2024

Posture-Informed Muscular Force Learning for Robust Hand Pressure Estimation

We present PiMForce, a novel framework that enhances hand pressure estimation by leveraging 3D hand posture information to augment forearm surface electromyography (sEMG) signals. Our approach utilizes detailed spatial information from 3D hand poses in conjunction with dynamic muscle activity from sEMG to enable accurate and robust whole-hand pressure measurements under diverse hand-object interactions. We also developed a multimodal data collection system that combines a pressure glove, an sEMG armband, and a markerless finger-tracking module. We created a comprehensive dataset from 21 participants, capturing synchronized data of hand posture, sEMG signals, and exerted hand pressure across various hand postures and hand-object interaction scenarios using our collection system. Our framework enables precise hand pressure estimation in complex and natural interaction scenarios. Our approach substantially mitigates the limitations of traditional sEMG-based or vision-based methods by integrating 3D hand posture information with sEMG signals. Video demos, data, and code are available online.

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arXiv2023

Robust Sound-Guided Image Manipulation

Recent successes suggest that an image can be manipulated by a text prompt, e.g., a landscape scene on a sunny day is manipulated into the same scene on a rainy day driven by a text input "raining". These approaches often utilize a StyleCLIP-based image generator, which leverages multi-modal (text and image) embedding space. However, we observe that such text inputs are often bottlenecked in providing and synthesizing rich semantic cues, e.g., differentiating heavy rain from rain with thunderstorms. To address this issue, we advocate leveraging an additional modality, sound, which has notable advantages in image manipulation as it can convey more diverse semantic cues (vivid emotions or dynamic expressions of the natural world) than texts. In this paper, we propose a novel approach that first extends the image-text joint embedding space with sound and applies a direct latent optimization method to manipulate a given image based on audio input, e.g., the sound of rain. Our extensive experiments show that our sound-guided image manipulation approach produces semantically and visually more plausible manipulation results than the state-of-the-art text and sound-guided image manipulation methods, which are further confirmed by our human evaluations. Our downstream task evaluations also show that our learned image-text-sound joint embedding space effectively encodes sound inputs.

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