Iconix: Controlling Semantics and Style in Progressive Icon Grids Generation
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918317493583872 |
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| author | Sun, Zhida Wang, Xiaodong Zhang, Zhenyao Lu, Min Lischinski, Dani Cohen-Or, Daniel Huang, Hui |
| author_facet | Sun, Zhida Wang, Xiaodong Zhang, Zhenyao Lu, Min Lischinski, Dani Cohen-Or, Daniel Huang, Hui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00738 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Iconix: Controlling Semantics and Style in Progressive Icon Grids Generation Sun, Zhida Wang, Xiaodong Zhang, Zhenyao Lu, Min Lischinski, Dani Cohen-Or, Daniel Huang, Hui Human-Computer Interaction Graphics 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. |
| title | Iconix: Controlling Semantics and Style in Progressive Icon Grids Generation |
| topic | Human-Computer Interaction Graphics |
| url | https://arxiv.org/abs/2602.00738 |