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arXiv · 2602.00738

Iconix: Controlling Semantics and Style in Progressive Icon Grids Generation

Zhida Sun·Xiaodong Wang·Zhenyao Zhang·Min Lu·Dani Lischinski·Daniel Cohen-Or·Hui Huang
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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.

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Zhida Sun, Xiaodong Wang, Zhenyao Zhang, Min Lu, Dani Lischinski, Daniel Cohen-Or, Hui Huang. 2026-01-31. Iconix: Controlling Semantics and Style in Progressive Icon Grids Generation. https://arxiv.org/abs/2602.00738

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