Iconix: Controlling Semantics and Style in Progressive Icon Grids Generation

2026-01-31

AI Summary

This work addresses how to support designers in efficiently exploring icon designs across the spectrum between concrete and abstract representations. The authors propose a generative system organized along two axes—semantic richness and visual complexity—that integrates semantic scaffolding with image-conditioned sequential generation to automatically produce progressively simplified icon sequences, thereby constructing a navigable two-dimensional icon grid. This approach enables joint control over both semantic content and visual style, significantly enhancing the efficiency and creativity of human-AI collaborative design. A user study (N=32) demonstrates that, compared to baseline methods, participants experienced lower cognitive load and generated design variants that were more coherent and creative.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Computer Vision: Visual Reasoning & Symbolic Representations

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Visual communication often needs stylistically consistent icons that span concrete and abstract meanings, for use in diverse contexts. We present Iconix, a human-AI co-creative system that organizes icon generation along two axes: semantic richness (what is depicted) and visual complexity (how much detail). Given a user-specified concept, Iconix constructs a semantic scaffold of related analytical perspectives and employs chained, image-conditioned generation to produce a coherent style of exemplars. Each exemplar is then automatically distilled into a progressive sequence, from detailed and elaborate to abstract and simple. The resulting two-dimensional grid exposes a navigable space, helping designers reason jointly about figurative content and visual abstraction. A within-subjects study (N = 32) found that compared to a baseline workflow, participants produced icon grids more creatively, reported lower workload, and explored a coherent range of design variations. We discuss implications for human-machine co-creative approaches that couple semantic scaffolding with progressive simplification to support visual abstraction.

Problem

• semantic control
• visual abstraction
• icon generation
• style consistency
• progressive simplification

Innovation

• semantic scaffolding
• progressive simplification
• image-conditioned generation
• human-AI co-creation
• visual abstraction