arXiv subjects

Jinwook Kim

Publications and source records attributed to Jinwook Kim.

At least 19 recordsLinked to original sources

arXiv2026

Point&Spawn: Mid-Air Reference-Free Object Instantiation Using Gaze and Hand Gestures in Extended Reality

Mid-air object instantiation in XR requires users to specify a 3D position without spatial references, such as surfaces or existing objects. We present Point&Spawn, a staged pipeline for pre-instantiation position specification through Direction Setting, Depth Setting, and Position Refinement within a continuous gesture flow. We evaluated six controller-free techniques combining Gaze or Non-Dominant Hand (NDH) direction setting with Ray Intersection, Relative Gain, or Drag&Hold depth setting in a user study (N=24) across Near and Far spawn depths. Relative Gain and Drag&Hold yielded faster and more accurate spawning, lower workload, higher usability, and greater preference than Ray Intersection. The shoulder-referenced NDH ray improved speed and coarse accuracy, whereas the viewpoint-based Gaze ray reduced hand movement with comparable final accuracy. Farther spawn depth imposed greater temporal costs as well as Gaze and accuracy costs with Ray Intersection. These findings offer empirical guidance for designing direction and depth control in spawning in XR.

cs.HC↗

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.

cs.HC↗

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.

cs.HC↗

arXiv2026

Mechanizing Typed Regulatory Actions for Security Tokens: Semantics, Falsification, and Bounded EVM Evidence

Security-token standards expose privileged controls without identifying the legal effect executed or the evidence and reversal obligations it carries. We formalize in Isabelle/HOL a reference execution semantics for the six ERC-8319 meanings: FREEZE, SEIZE, CONFISCATE, LIQUIDATE, RESTRICT, and RECOVER. It distinguishes applied, rejected, and operational-failure outcomes and mechanizes action-specific reversals, replay and epoch rules, complete frames, case-local terminality, and final receipts; the session builds without unproved placeholders or additional axioms. An indistinguishability theorem shows that bound kernel inputs cannot establish external facts about title, settlement, or entitlement. Constructive witnesses and direct mutations establish reachability and sensitivity for the declared fault set. For a successor ERC-TRUST Solidity/EVM candidate, we report separately scoped Foundry, Certora, Kontrol/KEVM, mutation, deterministic-build, runtime-identity, and independent-reproduction evidence. All twelve named evidence lanes pass with none pending, while the 74-row obligation ledger remains conditional: 70 rows are closed, two runtime-link rows remain successor obligations, and two are inapplicable. The Native runtime is bound separately from an ERC-3643 interoperability reference that explicitly reports Partial and full=false, not Verified Full. These results do not establish complete Isabelle-to-Solidity-to-EVM refinement, compiler correctness, audit completion, production readiness, deployment verification, or external legal truth. They provide a machine-checked domain semantics and an explicit map of proved, bounded, assumed, and open results.

cs.CR↗

arXiv2026

RLDX-1 Technical Report

While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intelligence (i.e. broad scene understanding and language-conditioned generalization) inherited from pre-trained Vision-Language Models, they still struggle with complex real-world tasks requiring broader functional capabilities (e.g. motion awareness, long-term memory, and physical sensing). To address this, we introduce RLDX-1, a general-purpose robotic policy for dexterous manipulation built on the Multi-Stream Action Transformer (MSAT), an architecture that unifies these capabilities by integrating heterogeneous modalities through modality-specific streams with cross-modal joint self-attention. RLDX-1 further combines this architecture with system-level design choices, including data synthesis for rare manipulation scenarios, learning procedures specialized for human-like manipulation, and inference optimizations for real-time deployment. Through empirical evaluation, we show that RLDX-1 consistently outperforms recent frontier VLAs (e.g. $\pi_{0.5}$ and GR00T N1.6) across both simulation benchmarks and real-world tasks that require broad functional capabilities beyond general versatility. In particular, RLDX-1 shows superiority in ALLEX humanoid tasks by achieving success rates of 86.8% while $\pi_{0.5}$ and GR00T N1.6 achieve around 40%, highlighting the ability of RLDX-1 to control a high-DoF humanoid robot under diverse functional demands. Together, these results position RLDX-1 as a promising step toward reliable VLAs for complex, contact-rich, and dynamic real-world dexterous manipulation.

cs.RO↗

arXiv2026

The Cross-Domain State Preservation Functor: A Mechanized Theory of Regulatory State Synchronization in Isabelle/HOL

A regulatory action on a tokenized asset, such as a freeze, a seizure, or a confiscation, must take effect on every domain holding it, or on none. We mechanize cross-domain state preservation in Isabelle/HOL as a functor: state machines are objects, structure-preserving synchronization maps are morphisms, and identity, composition and associativity hold as theorems. Four results follow. Safety: a regulatory transition is reflected across all connected domains, with roundtrip preservation, N-domain consistency, per-asset isolation and preserved terminal states, so regulatory finality survives. Liveness: deterministic conflict resolution and starvation freedom under f < n/3 Byzantine nodes, a cardinality-bounded dishonest tag rather than a message-level adversary, and a fair-leader assumption, with n >= 3f+1 shown to make that fairness assumption inhabitable. Convergence: from an arbitrary unlocked configuration, with no initial consistency assumed, synchronization reaches a valid state in boundedly many steps, along a recovery path that neither manufactures nor erases confiscations. Hierarchy: a tower of synchronization-degree functors linked by natural transformations closed under composition, a layer that, to our knowledge, Lochbihler and Maric's ADS_Functor does not develop, with a one-directional degree monotonicity. We couple the functor to that authenticated data structure, instantiated on a recursive Canton transaction-tree model with a declared consensus-scope limit. Eighteen interpretations, an external-domain instance and deletion-sensitive witnesses keep the theorems off the empty class. The model is atomic and its refinement to code is unproven. Ten theory files build without sorry or oops, released as a versioned Isabelle session.

cs.CR↗

arXiv2026

A Regulatory Compliance Protocol for Asset Interoperability Between Traditional and Decentralized Finance in Tokenized Capital Markets

There have been various attempts at token standards on numerous blockchain platforms today to fundamentally change the way assets are traded in the traditional capital markets, but there is a lack of research and resolution on regulatory issues that become the common foundation for interoperability and reusable standards. Our proposal, Regulatory Compliance Protocol (RCP), is based on the regulations and reports of 15 global financial institutions and standardizes recommendations and guidelines involving the overall asset tokenization of TradFi and DeFi into five regulatory groups: Traceability, Privacy, Enforceability, Finality and Tokenizability, compiling them into 31 items and presenting a benchmark for technology and standards as an underlying protocol. To review the legality and effectiveness of RCP, it was validated based on three tokenization and trading scenarios, and by benchmarking existing asset-tokenization standards (ERC-20, ERC-7943, ERC-1400, and ERC-3643) against RCP, it makes explicit which regulatory requirements each standard addresses at the token level and which remain inherently off-chain.

cs.CY↗

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.

cs.HC↗

arXiv2026

Streamlined Facial Data Collection based on Utterance and Emotional Data for Human-to-Avatar Reconstruction

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.

cs.HC↗

arXiv2025

SoccerNet 2025 Challenges Results

The SoccerNet 2025 Challenges mark the fifth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in football video understanding. This year's challenges span four vision-based tasks: (1) Team Ball Action Spotting, focused on detecting ball-related actions in football broadcasts and assigning actions to teams; (2) Monocular Depth Estimation, targeting the recovery of scene geometry from single-camera broadcast clips through relative depth estimation for each pixel; (3) Multi-View Foul Recognition, requiring the analysis of multiple synchronized camera views to classify fouls and their severity; and (4) Game State Reconstruction, aimed at localizing and identifying all players from a broadcast video to reconstruct the game state on a 2D top-view of the field. Across all tasks, participants were provided with large-scale annotated datasets, unified evaluation protocols, and strong baselines as starting points. This report presents the results of each challenge, highlights the top-performing solutions, and provides insights into the progress made by the community. The SoccerNet Challenges continue to serve as a driving force for reproducible, open research at the intersection of computer vision, artificial intelligence, and sports. Detailed information about the tasks, challenges, and leaderboards can be found at https://www.soccer-net.org, with baselines and development kits available at https://github.com/SoccerNet.

cs.CV↗

arXiv2025

Acidity-Mediated Metal Oxide Heterointerfaces: Roles of Substrates and Surface Modification

Although strong modulation of interfacial electron concentrations by the relative acidity of surface additives has been suggested, direct observation of corresponding changes in surface conductivity, crucial for understanding the role of local space charge, has been lacking. Here, we introduce a model platform comprising well-aligned mixed ionic-electronic conducting $\mathrm{Pr}_{0.2}\mathrm{Ce}_{0.8}\mathrm{O}_{2-\delta}$ nanowire arrays ($\mathrm{PCO}_{\mathrm{NA}}$) to show that acidity-modulated heterointerfaces predict electron depletion or accumulation, resulting in tunable electrical properties. We confirm three orders of magnitude increased $\mathrm{PCO}_{\mathrm{NA}}$ conductivity with basic $\mathrm{Li}_{2}\mathrm{O}$ infiltration. Moreover, the relative acidity of the insulating substrate supporting the $\mathrm{PCO}_{\mathrm{NA}}$ strongly influences its electronic properties as well. This strategy is further validated in purely ionic-conducting nanostructured ceria as well as $\mathrm{PCO}_{\mathrm{NA}}$. We suggest that observed conductivity changes stem not only from acidity-mediated space charge potentials at heterointerfaces but also from grain boundaries, chemically-modulated by cation in-diffusion. These findings have broad implications for how substrate and surface treatment choices can alter the conductive properties of nanostructured functional oxides.

cond-mat.mtrl-sci↗

arXiv2025

PinchCatcher: Enabling Multi-selection for Gaze+Pinch

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.

cs.HC↗

arXiv2025

Structure-from-Sherds++: Robust Incremental 3D Reassembly of Axially Symmetric Pots from Unordered and Mixed Fragment Collections

Reassembling multiple axially symmetric pots from fragmentary sherds is crucial for cultural heritage preservation, yet it poses significant challenges due to thin and sharp fracture surfaces that generate numerous false positive matches and hinder large-scale puzzle solving. Existing global approaches, which optimize all potential fragment pairs simultaneously or data-driven models, are prone to local minima and face scalability issues when multiple pots are intermixed. Motivated by Structure-from-Motion (SfM) for 3D reconstruction from multiple images, we propose an efficient reassembly method for axially symmetric pots based on iterative registration of one sherd at a time, called Structure-from-Sherds++ (SfS++). Our method extends beyond simple replication of incremental SfM and leverages multi-graph beam search to explore multiple registration paths. This allows us to effectively filter out indistinguishable false matches and simultaneously reconstruct multiple pots without requiring prior information such as base or the number of mixed objects. Our approach achieves 87% reassembly accuracy on a dataset of 142 real fragments from 10 different pots, outperforming other methods in handling complex fracture patterns with mixed datasets and achieving state-of-the-art performance. Code and results can be found in our project page https://sj-yoo.info/sfs/.

eess.IV↗

arXiv2024

Effects of Multisensory Feedback on the Perception and Performance of Virtual Reality Hand-Retargeted Interaction

Retargeting methods that modify the visual representation of real movements have been widely used to expand the interaction space and create engaging virtual reality experiences. For optimal user experience and performance, it is essential to specify the perception of retargeting and utilize the appropriate range of modification parameters. However, previous studies mostly concentrated on whether users perceived the target sense or not and rarely examined the perceptual accuracy and sensitivity to retargeting. Moreover, it is unknown how the perception and performance in hand-retargeted interactions are influenced by multisensory feedback. In this study, we used rigorous psychophysical methods to specify users' perceptual accuracy and sensitivity to hand-retargeting and provide acceptable ranges of retargeting parameters. We also presented different multisensory feedback simultaneously with the retargeting to probe its effect on users' perception and task performance. The experimental results showed that providing continuous multisensory feedback, proportionate to the distance between the virtual hand and the targeted destination, heightened the accuracy of users' perception of hand retargeting without altering their perceptual sensitivity. Furthermore, the utilization of multisensory feedback considerably improved the precision of task performance, particularly at lower gain factors. Based on these findings, we propose design guidelines and potential applications of VR hand-retargeted interactions and multisensory feedback for optimal user experience and performance.

cs.HC↗

arXiv2022

ExpTrialMng: A Universal Experiment Trial Manager for AR/VR/MR Experiments based on Unity

Based on the improvement of recent virtual and augmented reality (VR and AR) Head Mounted Display (HMD), there have been attempts to adopt VR and AR in various fields. Since VR and AR could provide more immersive experimental environments and stimuli than 2D settings in a cost-efficient way, psychological and cognitive researchers are particularly interested in using these platforms. However, there is still an entry barrier for researchers who are not familiar with Unity programming, and current VR/AR HMDs could also cause unexpected errors during the experiment. Therefore, we developed a Unity library that can be adopted in various experiments universally and assist researchers in developing their own. Our library provides functions related to trial assignment and results saving. That way, researchers can easily implement the essential functions of their psychological experiments. We also made a function that enables proceeding with the experiment from a specific trial point to handle unexpected errors caused by HMD tracking loss issues during the experiment. We expect our library could invite researchers from various disciplines and help them acquire valuable insights in VR/AR environments.

cs.HC↗

arXiv2022

The Effects of Spatial Configuration on Relative Translation Gain Thresholds in Redirected Walking

In this study, we explore how spatial configurations can be reflected in determining the threshold range of Relative Translation Gains (RTGs), a translation gain-based Redirected Walking (RDW) technique that scales the user's movement in Virtual Reality (VR) in different ratios for width and depth. While previous works have shown that various cognitive factors or individual differences influence the RDW threshold, constructive studies investigating the impact of the environmental composition on the RDW threshold with regard to the user's visual perception were lacking. Therefore, we examined the effect of spatial configurations on the RTG threshold by analyzing the participant's responses and gaze distribution data in two user studies. The first study concerned the size of the virtual room and the existence of objects within it, and the second study focused on the combined impact of room size and the spatial layout. Our results show that three compositions of spatial configuration (size, object existence, spatial layout) significantly affect the RTG threshold range. Based on our findings, we proposed virtual space rescaling guidelines to increase the range of adjustable movable space with RTGs for developers: placing distractors in the room, setting the perceived movable space to be larger than the adjusted movable space if it's an empty room, and avoid placing objects together as centered layout. Our findings can be used to adaptively rescale VR users' space according to the target virtual space's configuration with a unified coordinate system that enables the utilization of physical objects in a virtual scene.

cs.HC↗

arXiv2022

Gait Events Prediction using Hybrid CNN-RNN-based Deep Learning models through a Single Waist-worn Wearable Sensor

Elderly gait is a source of rich information about their physical and mental health condition. As an alternative to the multiple sensors on the lower body parts, a single sensor on the pelvis has a positional advantage and an abundance of information acquirable. This study aimed to explore a way of improving the accuracy of gait event detection in the elderly using a single sensor on the waist and deep learning models. Data was gathered from elderly subjects equipped with three IMU sensors while they walked. The input was taken only from the waist sensor was used to train 16 deep-learning models including CNN, RNN, and CNN-RNN hybrid with or without the Bidirectional and Attention mechanism. The groundtruth was extracted from foot IMU sensors. Fairly high accuracy of 99.73% and 93.89% was achieved by the CNN-BiGRU-Att model at the tolerance window of $\pm$6TS ($\pm$6ms) and $\pm$1TS ($\pm$1ms) respectively. Advancing from the previous studies exploring gait event detection, the model showed a great improvement in terms of its prediction error having an MAE of 6.239ms and 5.24ms for HS and TO events respectively at the tolerance window of $\pm$1TS. The results showed that the use of CNN-RNN hybrid models with Attention and Bidirectional mechanisms is promising for accurate gait event detection using a single waist sensor. The study can contribute to reducing the burden of gait detection and increase its applicability in future wearable devices that can be used for remote health monitoring (RHM) or diagnosis based thereon.

eess.SP↗

arXiv2022

Effects of Virtual Room Size and Objects on Relative Translation Gain Thresholds in Redirected Walking

This paper investigates how the size of virtual space and objects within it affect the threshold range of relative translation gains, a Redirected Walking (RDW) technique that scales the user's movement in virtual space in different ratios for the width and depth. While previous studies assert that a virtual room's size affects relative translation gain thresholds on account of the virtual horizon's location, additional research is needed to explore this assumption through a structured approach to visual perception in Virtual Reality (VR). We estimate the relative translation gain thresholds in six spatial conditions configured by three room sizes and the presence of virtual objects (3 X 2), which were set according to differing Angles of Declination (AoDs) between eye-gaze and the forward-gaze. Results show that both size and virtual objects significantly affect the threshold range, it being greater in the large-sized condition and furnished condition. This indicates that the effect of relative translation gains can be further increased by constructing a perceived virtual movable space that is even larger than the adjusted virtual movable space and placing objects in it. Our study can be applied to adjust virtual spaces in synchronizing heterogeneous spaces without coordinate distortion where real and virtual objects can be leveraged to create realistic mutual spaces.

cs.HC↗