iPoster: Content-Aware Layout Generation for Interactive Poster Design via Graph-Enhanced Diffusion Models
arXiv:2603.29469v1 Announce Type: new Abstract: We present iPoster, an interactive layout generation framework that empowers users to guide content-aware poster layout design by specifying flexible constraints. iPoster enables users to specify partial intentions within the intention module, such as element categories, sizes, positions, or coarse initial drafts. Then, the generation module instantly generates refined, context-sensitive layouts that faithfully respect these constraints. iPoster employs a unified graph-enhanced diffusion architecture that supports various design tasks under user-specified constraints. These constraints are enforced through masking strategies that precisely preserve user input at every denoising step. A cross content-aware attention module aligns generated ele
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Abstract:We present iPoster, an interactive layout generation framework that empowers users to guide content-aware poster layout design by specifying flexible constraints. iPoster enables users to specify partial intentions within the intention module, such as element categories, sizes, positions, or coarse initial drafts. Then, the generation module instantly generates refined, context-sensitive layouts that faithfully respect these constraints. iPoster employs a unified graph-enhanced diffusion architecture that supports various design tasks under user-specified constraints. These constraints are enforced through masking strategies that precisely preserve user input at every denoising step. A cross content-aware attention module aligns generated elements with salient regions of the canvas, ensuring visual coherence. Extensive experiments show that iPoster not only achieves state-of-the-art layout quality, but offers a responsive and controllable framework for poster layout design with constraints.
Subjects:
Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
Cite as: arXiv:2603.29469 [cs.HC]
(or arXiv:2603.29469v1 [cs.HC] for this version)
https://doi.org/10.48550/arXiv.2603.29469
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA '26), April 13--17, 2026, Barcelona, Spain
Related DOI:
https://doi.org/10.1145/3772363.3799094
DOI(s) linking to related resources
Submission history
From: Xudong Zhou [view email] [v1] Tue, 31 Mar 2026 09:14:30 UTC (2,744 KB)
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Is Turboquant really a game changer?
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