Takefumi Hiraki

Yong-Hao Hu, Sotaro Yokoi, Yuji Hatada et al.

Online experiments using metaverse platforms have gained significant traction in Human-Computer Interaction and Virtual Reality (VR) research. However, current research workflows are highly fragmented, as researchers must use separate tools for system implementation, participant recruitment, experiment execution, and data collection, reducing consistency and increasing workload. We present LUIDA (Large-scale Unified Infrastructure for Digital Assessments), a metaverse-based framework that integrates these fragmented processes. LUIDA automatically allocates interconnected virtual environments for parallel experiment execution and provides implementation templates adaptable to various VR research domains, requiring minimal metaverse development expertise. Our evaluation included two studies using a prototype built on Cluster, the commercial metaverse platform. First, VR researchers using LUIDA to develop and run experiments reported high usability scores (SUS: 73.75) and moderate workload (NASA-TLX: 24.11) for overall usage, with interviews confirming streamlined workflows compared to traditional laboratory experiments. Second, we conducted three replicated experiments with public Cluster users, each recruiting approximately 200 participants within one week. These experiments produced results that closely matched the original studies, validating the experimental integrity of LUIDA across research domains. After technical refinements, we plan to release LUIDA as an open platform, providing a standardized protocol to improve research efficiency and experimental reproducibility in VR studies.

Michikuni Eguchi, Takekazu Kitagishi, Yuichi Hiroi et al.

The tactile sensation of clothing is critical to wearer comfort. To reveal physical properties that make clothing comfortable, systematic collection of tactile data during sliding motion is required. We propose a robotic arm-based system for collecting tactile data from intact garments. The system performs stroking measurements with a simulated fingertip while precisely controlling speed and direction, enabling creation of motion-labeled, multimodal tactile databases. Machine learning evaluation showed that including motion-related parameters improved identification accuracy for audio and acceleration data, demonstrating the efficacy of motion-related labels for characterizing clothing tactile sensation. This system provides a scalable, non-destructive method for capturing tactile data of clothing, contributing to future studies on fabric perception and reproduction.

Hikari Yanagawa, Yuichi Hiroi, Satomi Tokida et al.

While commercial metaverse platforms offer diverse user-generated content, they lack effective navigation assistance that can dynamically adapt to users' interests and intentions. Although previous research has investigated on-demand agents in controlled environments, implementation in commercial settings with diverse world configurations and platform constraints remains challenging. We present Navigation Pixie, an on-demand navigation agent employing a loosely coupled architecture that integrates structured spatial metadata with LLM-based natural language processing while minimizing platform dependencies, which enables experiments on the extensive user base of commercial metaverse platforms. Our cross-platform experiments on commercial metaverse platform Cluster with 99 PC client and 94 VR-HMD participants demonstrated that Navigation Pixie significantly increased dwell time and free exploration compared to fixed-route and no-agent conditions across both platforms. Subjective evaluations revealed consistent on-demand preferences in PC environments versus context-dependent social perception advantages in VR-HMD. This research contributes to advancing VR interaction design through conversational spatial navigation agents, establishes cross-platform evaluation methodologies revealing environment-dependent effectiveness, and demonstrates empirical experimentation frameworks for commercial metaverse platforms.

Tatsuyuki Ueda, Daisuke Iwai, Takefumi Hiraki et al.

Aiming at realizing novel vision augmentation experiences, this paper proposes the IlluminatedFocus technique, which spatially defocuses real-world appearances regardless of the distance from the user's eyes to observed real objects. With the proposed technique, a part of a real object in an image appears blurred, while the fine details of the other part at the same distance remain visible. We apply Electrically Focus-Tunable Lenses (ETL) as eyeglasses and a synchronized high-speed projector as illumination for a real scene. We periodically modulate the focal lengths of the glasses (focal sweep) at more than 60 Hz so that a wearer cannot perceive the modulation. A part of the scene to appear focused is illuminated by the projector when it is in focus of the user's eyes, while another part to appear blurred is illuminated when it is out of the focus. As the basis of our spatial focus control, we build mathematical models to predict the range of distance from the ETL within which real objects become blurred on the retina of a user. Based on the blur range, we discuss a design guideline for effective illumination timing and focal sweep range. We also model the apparent size of a real scene altered by the focal length modulation. This leads to an undesirable visible seam between focused and blurred areas. We solve this unique problem by gradually blending the two areas. Finally, we demonstrate the feasibility of our proposal by implementing various vision augmentation applications.