OnomaCompass: A Texture Exploration Interface that Shuttles between Words and Images

TL;DR

OnomaCompass is a web-based system that links sound-symbolic onomatopoeia and visual textures to facilitate early-stage material discovery, reducing workload and enhancing user experience in texture exploration.

Contribution

The paper introduces OnomaCompass, a novel interface that connects words and images through latent-space maps for texture exploration, bypassing the need for complex AI prompts.

Findings

Significantly reduced user workload and frustration.

Enhanced hedonic user experience during exploration.

Facilitated externalization of sensory expectations and serendipitous discovery.

Abstract

Humans can finely perceive material textures, yet articulating such somatic impressions in words is a cognitive bottleneck in design ideation. We present OnomaCompass, a web-based exploration system that links sound-symbolic onomatopoeia and visual texture representations to support early-stage material discovery. Instead of requiring users to craft precise prompts for generative AI, OnomaCompass provides two coordinated latent-space maps--one for texture images and one for onomatopoeic term--built from an authored dataset of invented onomatopoeia and corresponding textures generated via Stable Diffusion. Users can navigate both spaces, trigger cross-modal highlighting, curate findings in a gallery, and preview textures applied to objects via an image-editing model. The system also supports video interpolation between selected textures and re-embedding of extracted frames to form an emergent exploration loop. We conducted a within-subjects study with 11 participants comparing OnomaCompass to a prompt-based image-generation workflow using Gemini 2.5 Flash Image ("Nano Banana"). OnomaCompass significantly reduced workload (NASA-TLX overall, mental demand, effort, and frustration; p < .05) and increased hedonic user experience (UEQ), while usability (SUS) favored the baseline. Qualitative findings indicate that OnomaCompass helps users externalize vague sensory expectations and promotes serendipitous discovery, but also reveals interaction challenges in spatial navigation. Overall, leveraging sound symbolism as a lightweight cue offers a complementary approach to Kansei-driven material ideation beyond prompt-centric generation.