Gen-lyph: A Human-AI Collaboration System for Creating Figurative Glyph-Based Visualizations

Figurative glyph-based visualizations (FGVs) convey data through recognizable visual objects, but their creation is labor-intensive and conceptually demanding. A central challenge lies in enabling figurative mapping of data variables onto distinct components of a generated visual object while ensuring that automatically produced designs remain aligned with the user intent. We present Gen-lyph, a human-AI collaboration system that combines generative models with interactive refinement to support semantically rich FGV design. Informed by formative interviews and an analysis of existing FGVs, we developed a fourphase generative pipeline comprising ideation, decomposition, encoding, and placement, integrating automated generation with user control. Gen-lyph allows users to guide figurative glyph generation iteratively, segment and assign data attributes, and progressively refine encodings and arrangements. Gen-lyph was evaluated through an expert review, a free exploration study, and a usage scenario.