arXiv subjects

Jun Rekimoto

Publications and source records attributed to Jun Rekimoto.

At least 19 recordsLinked to original sources

arXiv2026

Flash Word : ESL Listening Support with Subtitle Skimming

Listening to English audio with English subtitles is thought to be effective for ESL students to improve listening comprehension skills. However, for those who learn English as a second language it can be hard to finish English subtitles. Also they are unlikely to focus on listening while being busy reading subtitles. We propose Flash Word, a subtitling system which generates and presents shortened intralingual subtitles for English audio by detecting keywords from the script. Flash Word extracts keywords from scripts using an existing algorithm, and shows them one at a time like a flashcard. The system calculates when the keywords are being pronounced and aligns them correctly. Flash Word aims to enable ESL students to focus better on listening by minimising the cost of reading subtitles.

cs.HC↗

arXiv2026

Warping the Workspace: Expanding Visual Access with Adjustable Reach Mapping for Humanoid Teleoperation

First-person humanoid teleoperation ties access to side and rear targets to operator rotation. We present coupled visual--motor remapping, pairing a panoramic azimuth warp with its inverse upper-limb yaw transform. A working prototype implements Global Zoom (GZ) and Focus+Context (F+C). In a within-subject study (N=8); 72 trials), GZ had the largest completion count (23/24; Fixed: 20/24; F+C: 21/24), whereas F+C was preferred by 6/8 participants. Therefore, completion and preference favored different mappings in this sample.

cs.RO↗

arXiv2026

ManzaiSet: A Multimodal Dataset of Viewer Responses to Japanese Manzai Comedy

We introduce ManzaiSet, a multimodal dataset of individual viewer responses to Japanese manzai comedy. The corpus contains 2,301 viewer-performance recordings from 241 native Japanese speakers, totaling 191.8 hours of facial video and microphone audio. Participants watched up to 10 shared professional performances in randomized order at home and rated each performance on a 0-100 scale. Exploratory analyses yielded three findings. First, viewers differed in both their average ratings and how much their ratings varied across performances. Viewers who rated one random half of the performances highly also tended to rate the other half highly within the same viewing sequence. Second, performances watched later in the sequence received higher ratings on average. Third, after multiple-comparison correction, no statistically significant differences in viewer responses were found between humor categories. ManzaiSet supports the study of individual differences by linking shared Japanese comedy performances to viewer ratings, viewing order, and audiovisual responses.

cs.CV↗

arXiv2026

"Just Like This": Manner Deixis at the Graphics Interface

People communicate how things should move by combining words with demonstrations: "open it like this." We present an interaction technique that brings this expressive resource to conversational 3D authoring. Building on "Put-That-There," our system combines speech, pointing, and spatiotemporal demonstrations to specify editable behavior. A hand movement supplies evidence for a mechanism's axis, pivot, range, and pace; an animated preview makes the interpretation inspectable. Users refine behavior through further words or demonstrations, and the system can request a demonstration to clarify intent. In a twelve-participant study comparing three input configurations, our system achieved 89% participant-declared completion versus 47% with speech alone, had the highest observed match rates on six categorical accuracy measures and tied on two, and was preferred by nine participants. This work makes demonstration part of an ongoing authoring conversation: behavior can be shown, inspected, and revised.

cs.HC↗

arXiv2026

Can People Distinguish Human and AI Agency in Humanoid Teleoperation? A Preliminary Study of Agency Perception

Can people distinguish between human and AI agency in humanoid teleoperation? To explore this question, we developed \textit{Ghost-in-the-Loop}, a teleoperation framework that supports both human-operated and AI-generated control of a robot's voice, facial expressions, and gestures while maintaining a consistent embodiment. We conducted a preliminary online study ($N=50$) in which participants viewed short interaction clips generated by either a Human Operator or an AI Control and judged the perceived source of control. Results suggest that participants often struggled to distinguish between the two conditions in brief interactions. Qualitative responses indicate that judgments were primarily influenced by perceived naturalness, temporal coordination, and consistency across speech, facial expression, and gesture. These findings provide initial insights into agency perception in embodied human--AI communication and motivate future investigations of blended human--AI telepresence systems.

cs.HC↗

arXiv2026

Beyond "Made with AI": Visualizing Provenance Density to Mitigate the Transparency Penalty

As generative AI makes polished prose cheap to produce, users can no longer rely on fluency as a proxy for truth. We call this failure mode the Fluency Trap: users trust fluent hallucinations while also discounting accurate content once it is disclosed as AI-generated. Binary ``Made with AI'' labels respond with authorship disclosure, but they do not show what supports a claim. We propose Provenance Density, an evidence-visualization interface that shows the density of verified claims in a text. In a user study with 81 participants, an idealized Provenance Density interface produced a large discernment gap between truth and fabrication ($+4.15$ points, $d=1.82$), whereas participants given no signal showed no detectable discrimination. A technical audit with 200 samples shows that retrieval density alone is insufficient; unexpectedly, the Consistency Veto carries most of the discriminative signal on dynamic queries. As AI-generated content becomes indistinguishable from human writing, effective transparency must move from authorship disclosure toward evidence visualization.

cs.AI↗

arXiv2026

NFAD: Nuisance-Filtered Anomaly Detection Under Distribution Shift

Recent advances in anomaly detection (AD) for industrial inspection have pushed performance on standard benchmarks toward saturation. However, strong benchmark performance does not necessarily translate to real-world deployment, as these benchmarks are primarily collected under controlled acquisition conditions. Changes in illumination, background, viewpoint, and other environmental factors can shift normal samples away from the learned normal distribution and cause false anomaly responses. We address AD under such distribution shifts by explicitly modeling nuisance variation from changing imaging conditions in feature space. Without anomaly labels or target-domain data, our Nuisance-Filtered Anomaly Detection (NFAD) framework estimates a nuisance subspace from matched feature displacements induced by content-preserving perturbations and suppresses its contribution to anomaly residuals at inference. The same subspace supports two complementary branches: full projection for image-level detection and selective suppression for pixel-level localization, preserving evidence of localized defects. On AeBAD-S, a benchmark specifically designed for AD under acquisition shifts, NFAD achieves 91.0\% image-level AUROC, establishing a new state of the art. Notably, this robustness does not come at the expense of conventional AD performance: NFAD remains competitive on standard benchmarks that do not explicitly evaluate distribution shift, including VisA, Real-IAD, and MVTec AD. These results show that explicitly suppressing such nuisance variation improves AD under distribution shift while preserving strong performance in standard settings.

cs.CV↗

arXiv2026

Profy: Interpretable Visualization of Expertise-Dependent Motor Skills Toward Supporting Piano Practice

The quality of piano performance depends on nuanced timing, articulation, and dynamic control, but practice feedback is often summary-based and hard to act on. We introduce Profy, a weakly supervised system that learns from take-level labels derived from aggregated listener ratings (expert-labeled vs. amateur-labeled) to produce time-aligned highlights for review during piano practice. We collected synchronized 1 kHz key-motion and audio from 73 pianists and used 1,083 valid takes for modeling and evaluation. The model outputs clip-level predictions together with evidence scores on a shared resampled model time base for visualization. On 20 amateur clips from short technique studies annotated by 21 expert pianists, the displayed highlight score aligns with passages that expert pianists marked for review despite training without localized labels (Pearson r=0.61, ROC-AUC 0.75). Rather than summarizing a take with a single global score, Profy helps learners decide where to inspect next by supporting scrubbing, looping, and focused replay of time-localized passages associated with expert-amateur differences.

cs.HC↗

arXiv2026

NasoVoce: A Nose-Mounted Low-Audibility Speech Interface for Always-Available Speech Interaction

Silent and whispered speech offer promise for always-available voice interaction with AI, yet existing methods struggle to balance vocabulary size, wearability, silence, and noise robustness. We present NasoVoce, a nose-bridge-mounted interface that integrates a microphone and a vibration sensor. Positioned at the nasal pads of smart glasses, it unobtrusively captures both acoustic and vibration signals. The nasal bridge, close to the mouth, allows access to bone- and skin-conducted speech and enables reliable capture of low-volume utterances such as whispered speech. While the microphone captures high-quality audio, it is highly sensitive to environmental noise. Conversely, the vibration sensor is robust to noise but yields lower signal quality. By fusing these complementary inputs, NasoVoce generates high-quality speech robust against interference. Evaluation with Whisper Large-v2, PESQ, STOI, and MUSHRA ratings confirms improved recognition and quality. NasoVoce demonstrates the feasibility of a practical interface for always-available, continuous, and discreet AI voice conversations.

cs.HC↗

arXiv2026

OpticalAging: Real-time Presbyopia Simulation for Inclusive Design via Tunable Lenses

Presbyopia, a common age-related vision condition affecting most people as they age, often remains inadequately understood by those unaffected. To help bridge the gap between abstract accessibility knowledge and a more grounded appreciation of perceptual challenges, this study presents OpticalAging, an optical see-through simulation approach. Unlike VR-based methods, OpticalAging uses dynamically controlled tunable lenses to simulate the first-person visual perspective of presbyopia's distance-dependent blur during real-world interaction, aiming to enhance awareness. While acknowledging critiques regarding simulation's limitations in fully capturing lived experience, we position this tool as a complement to user-centered methods. Our user study (N = 19, 18-35 years old) provides validation: quantitative measurements show statistically significant changes in near points across three age modes (40s, 50s, 60s), while qualitative results suggest increases in self-reported understanding and awareness of perceptual challenges among participants. The integration of our tool into a design task showcases its potential applicability within age-inclusive design workflows when used critically alongside direct user engagement.

cs.HC↗

arXiv2026

Beyond Symbols: Motion Perception Cues Enhance Dual-Task Performance with Wearable Directional Guidance

Directional cues are crucial for environmental interaction. Conventional methods rely on symbolic visual or auditory reminders that require semantic interpretation, a process that proves challenging in demanding dual-tasking scenarios. We introduce a novel alternative for conveying directional cues on wearable displays: directly triggering motion perception using monocularly presented peripheral stimuli. This approach is designed for low visual interference, with the goal of reducing the need for gaze-switching and the complex cognitive processing associated with symbols. User studies demonstrate our method's potential to robustly convey directional cues. Compared to a conventional arrow-based technique in a demanding dual-task scenario, our motion-based approach resulted in significantly more accurate interpretation of these directional cues ($p=.008$) and showed a trend towards reduced errors on the concurrent primary task ($p=.066$).

cs.HC↗

arXiv2025

Panel-by-Panel Souls: A Performative Workflow for Expressive Faces in AI-Assisted Manga Creation

Current text-to-image models struggle to render the nuanced facial expressions required for compelling manga narratives, largely due to the ambiguity of language itself. To bridge this gap, we introduce an interactive system built on a novel, dual-hybrid pipeline. The first stage combines landmark-based auto-detection with a manual framing tool for robust, artist-centric face preparation. The second stage maps expressions using the LivePortrait engine, blending intuitive performative input from video for fine-grained control. Our case study analysis suggests that this integrated workflow can streamline the creative process and effectively translate narrative intent into visual expression. This work presents a practical model for human-AI co-creation, offering artists a more direct and intuitive means of ``infusing souls'' into their characters. Our primary contribution is not a new generative model, but a novel, interactive workflow that bridges the gap between artistic intent and AI execution.

cs.HC↗

arXiv2025

Semantic Glitch: Agency and Artistry in an Autonomous Pixel Cloud

While mainstream robotics pursues metric precision and flawless performance, this paper explores the creative potential of a deliberately "lo-fi" approach. We present the "Semantic Glitch," a soft flying robotic art installation whose physical form, a 3D pixel style cloud, is a "physical glitch" derived from digital archaeology. We detail a novel autonomous pipeline that rejects conventional sensors like LiDAR and SLAM, relying solely on the qualitative, semantic understanding of a Multimodal Large Language Model to navigate. By authoring a bio-inspired personality for the robot through a natural language prompt, we create a "narrative mind" that complements the "weak," historically, loaded body. Our analysis begins with a 13-minute autonomous flight log, and a follow-up study statistically validates the framework's robustness for authoring quantifiably distinct personas. The combined analysis reveals emergent behaviors, from landmark-based navigation to a compelling "plan to execution" gap, and a character whose unpredictable, plausible behavior stems from a lack of precise proprioception. This demonstrates a lo-fi framework for creating imperfect companions whose success is measured in character over efficiency.

cs.RO↗

arXiv2025

Pinching Visuo-haptic Display: Investigating Cross-Modal Effects of Visual Textures on Electrostatic Cloth Tactile Sensations

This paper investigates how visual texture presentation influences tactile perception when interacting with electrostatic cloth displays. We propose a visuo-haptic system that allows users to pinch and rub virtual fabrics while feeling realistic frictional sensations modulated by electrostatic actuation. Through a user study, we examined the cross-modal effects between visual roughness and perceived tactile friction. The results demonstrate that visually rough textures amplify the perceived frictional force, even under identical electrostatic stimuli. These findings contribute to the understanding of multimodal texture perception and provide design insights for haptic feedback in virtual material interfaces.

cs.HC↗

arXiv2025

Juicy Text: Onomatopoeia and Semantic Text Effects for Juicy Player Experiences

Juiciness is visual pizzazz used to improve player experience and engagement in games. Most research has focused on juicy particle effects. However, text effects are also commonly used in games, albeit not always juiced up. One type is onomatopoeia, a well-defined element of human language that has been translated to visual media, such as comic books and games. Another is semantic text, often used to provide performance feedback in games. In this work, we explored the relationship between juiciness and text effects, aiming to replicate juicy user experiences with text-based juice and combining particle and text juice. We show in a multi-phase within-subjects experiment that users rate juicy text effects similarly to particles effects, with comparable performance, and more reliable feedback. We also hint at potential improvement in user experience when both are combined, and how text stimuli may be perceived differently than other visual ones. We contribute empirical findings on the juicy-text connection in the context of visual effects for interactive media.

cs.HC↗

arXiv2025

SakugaFlow: A Stagewise Illustration Framework Emulating the Human Drawing Process and Providing Interactive Tutoring for Novice Drawing Skills

While current AI illustration tools can generate high-quality images from text prompts, they rarely reveal the step-by-step procedure that human artists follow. We present SakugaFlow, a four-stage pipeline that pairs diffusion-based image generation with a large-language-model tutor. At each stage, novices receive real-time feedback on anatomy, perspective, and composition, revise any step non-linearly, and branch alternative versions. By exposing intermediate outputs and embedding pedagogical dialogue, SakugaFlow turns a black-box generator into a scaffolded learning environment that supports both creative exploration and skills acquisition.

cs.HC↗

arXiv2025

MaskClip: Detachable Clip-on Piezoelectric Sensing of Mask Surface Vibrations for Real-time Noise-Robust Speech Input

Masks are essential in medical settings and during infectious outbreaks but significantly impair speech communication, especially in environments with background noise. Existing solutions often require substantial computational resources or compromise hygiene and comfort. We propose a novel sensing approach that captures only the wearer's voice by detecting mask surface vibrations using a piezoelectric sensor. Our developed device, MaskClip, employs a stainless steel clip with an optimally positioned piezoelectric sensor to selectively capture speech vibrations while inherently filtering out ambient noise. Evaluation experiments demonstrated superior performance with a low Character Error Rate of 6.1\% in noisy environments compared to conventional microphones. Subjective evaluations by 102 participants also showed high satisfaction scores. This approach shows promise for applications in settings where clear voice communication must be maintained while wearing protective equipment, such as medical facilities, cleanrooms, and industrial environments.

cs.SD↗

arXiv2025

Real-Time Word-Level Temporal Segmentation in Streaming Speech Recognition

Rich-text captions are essential to help communication for Deaf and hard-of-hearing (DHH) people, second-language learners, and those with autism spectrum disorder (ASD). They also preserve nuances when converting speech to text, enhancing the realism of presentation scripts and conversation or speech logs. However, current real-time captioning systems lack the capability to alter text attributes (ex. capitalization, sizes, and fonts) at the word level, hindering the accurate conveyance of speaker intent that is expressed in the tones or intonations of the speech. For example, ''YOU should do this'' tends to be considered as indicating ''You'' as the focus of the sentence, whereas ''You should do THIS'' tends to be ''This'' as the focus. This paper proposes a solution that changes the text decorations at the word level in real time. As a prototype, we developed an application that adjusts word size based on the loudness of each spoken word. Feedback from users implies that this system helped to convey the speaker's intent, offering a more engaging and accessible captioning experience.

cs.HC↗