Instrumental music conveys rich affective experiences through acoustic cues, yet instrumental passages often remain inaccessible to Deaf and Hard-of-Hearing (DHH) audiences. Although captioning practices for vocal songs have expanded, instrumental music remains largely uncaptioned, with no established criteria for representing musical content in text. We propose µCap (Music Captions), an automatic instrumental music captioning system that transforms instrumental audio into time-aligned, non-lexical textual renderings enhanced with simple visuals. Drawing on Preliminary surveys with DHH individuals and expert group discussions, we developed a phonetic-like captioning schema grounded in music sound analysis and linguistics. We then implemented µCap using audio feature extraction and a retrieval-augmented generation pipeline to produce expressive, sound-mimetic captions. Two user evaluations with DHH participants (n=20 and n=15) showed that µCap enhanced music appreciation, immersion, and perceived presence of acoustic detail. This work contributes empirical evidence and insights for designing caption-based visual representations that make instrumental music more accessible.

We present VisiPrint, a tool for appearance-first previews of 3D-printed objects. Existing print preview slicers focus on toolpaths, not appearance, while pure rendering software is complex and cannot automatically reproduce slicing patterns. Prior work highlights persistent gaps between digital previews and printed results, such as color shifts, gloss/translucency changes, and layer-line highlights, motivating the creation of VisiPrint, an appearance-focused support tool. The VisiPrint algorithm combines slicer screenshots with filament photos via a custom diffusion-based synthesis pipeline. We present both a standalone user interface for VisiPrint compatible with any slicer and an Ultimaker Cura Plugin. We evaluate VisiPrint through a user study showing it is significantly faster, easier to use, and more faithful than alternatives: within a time-limit, participants completed 100% of preview tasks with VisiPrint, versus 63% with Cura and 13% with Blender. VisiPrint narrows the gap between design intent and printed appearance, complementing settings-centric tools with appearance-driven decision support.

3D printing onto pre-stretched fabrics has emerged as a promising technique for fabricating self-shaping textiles. However, resulting morphing behaviors are often dictated by heuristics or arbitrarily selected parameters. We present PixBric, a pixel-based design framework that enables precise morphological control through tessellated primitive geometries printed onto biaxially stretched fabrics. Upon release, these units buckle into programmed 3D forms including undulations, curling, and bistable snapping. PixBric integrates parametric modeling, mechanical simulation, and empirical evaluation to map geometric parameters to deformation outcomes. We demonstrate applications spanning morphable typography, wearable rings, and reconfigurable surfaces. PixBric bridges digital simulation (tide) with the mechanical constraints of elastic substrates (tied), transforming complex material behaviors into accessible tools for learning, experimentation, and creative fabrication.

Affective and prosodic captions convey not only what a speaker says, but also how they say it-louder words may appear thicker, quieter ones thinner; angry in red, calm in blue.These captions can improve access, satisfaction, and engagement for d/Deaf and Hard-of-Hearing (dhh) users.While prior work has explored their design space, it has focused largely on dhh participants in North America, limiting generalizability beyond English and Latin-based scripts.To uncover the role of culture and language, we ran an exploratory study with 49 dhh participants from North America and South Korea using CuCap, a tool that allowed them to personalize which speech features were displayed, and how.While emotion visualization was a universally favored choice, confirming prior findings, prosody preferences varied across cultures, reflecting linguistic and hearing factors.These findings point to the need for flexible captioning systems that account for cultural, linguistic, and individual differences.

Gesture data collection in a controlled lab environment often restricts participants to performing gestures in a uniform or biased manner, resulting in gesture data which may not sufficiently reflect gesture variability to build robust gesture recognition models. Crowdsourcing has been widely accepted as an efficient high-sample-size method for collecting more representative and variable data. In this study, we evaluated the effectiveness of crowdsourcing for gesture data collection, specifically for gesture variability. When compared to a controlled lab environment, crowdsourcing resulted in improved recognition performance of 8.98% and increased variability for various gesture features, eg, a 142% variation increase for Quantity of Movement. Integrating a supplemental gesture data collection methodology known as Styling Words increased recognition performance by an additional 2.94%. The study also investigated the efficacy of gesture collection methodologies and gesture memorization paradigms.

Captions help deaf and hard-of-hearing (DHH) individuals visually communicate voice information to better understand video content. In speech, the literal content and paralinguistic cues (e.g., pitch and nuance) work together to create real intention. However, current captions are limited in their capacity to deliver fine nuances because they cannot fully convey these paralinguistic cues. This paper proposes an audio-visualized caption system that automatically visualizes paralinguistic cues into various caption elements (thickness, height, font type and motion). A comparative study with 20 DHH participants demonstrates how our system supports DHH individuals to be better accessible to paralinguistic cues while watching videos. Particularly in the case of formal talks, they could accurately identify the speaker’s nuance more often compared to current captions, without any practice or training. Addressing some issues on legibility and familiarity, the proposed caption system has potentials to enrich DHH individuals’ video watching experience more as hearing people enjoy.