Jun Rekimoto

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.

Transmitting fabric tactile sensation is difficult because of fabric’s complex properties: softness, delicacy, and a relatively uniform surface texture. We propose Pinching Tactile Display (PTD), a conductive fabric system that enables end-to-end transmission of vibration signals measured from fabric. In a user study, we found that perceived roughness can be modulated within 2.1–3.0 on a Likert scale (suggestively comparable to fine-textile differences), and that this modulation arises from voltage-driven bumpiness and frequency-driven stickiness, with largely additive effects. We also found a clear correlation between input vibration signals and finger vibrations, demonstrating that input vibrations are reliably transmitted to the user’s finger. These results validate the system as a viable approach for end-to-end fabric tactile transmission and point to potential applications such as remote fabric exploration, VR, and fashion e-commerce.

In disaster response, agricultural work, and patrol in depopulated areas, workers frequently encounter localized obstacles such as ditches and rubble that are difficult to overcome by walking yet do not warrant full flight mobility. We present a wearable Partial Gravity Assist system using a backpack-type hexacopter that reduces the user’s effective body weight to approximately two-thirds while keeping thrust below body weight, thereby augmenting natural jumping without transitioning to flight. In vertical jump experiments with two participants, the Assist condition improved maximum jump height by 10–16% and jump time by 70–80%. This work demonstrates that partial gravity assist enables jumping as an extension of walking for crossing localized gaps.

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.

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.