GRIDS: Interactive Layout Design with Integer Programming
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Abstract (arXiv)
Grid layouts are used by designers to spatially organise user interfaces when sketching and wireframing. However, their design is largely time consuming manual work. This is challenging due to combinatorial explosion and complex objectives, such as alignment, balance, and expectations regarding positions. This paper proposes a novel optimisation approach for the generation of diverse grid-based layouts. Our mixed integer linear programming (MILP) model offers a rigorous yet efficient method for grid generation that ensures packing, alignment, grouping, and preferential positioning of elements. Further, we present techniques for interactive diversification, enhancement, and completion of grid layouts (Figure 1). These capabilities are demonstrated using GRIDS1, a wireframing tool that provides designers with real-time layout suggestions. We report findings from a ratings study (N = 13) and a design study (N = 16), lending evidence for the benefit of computational grid generation during early stages of design.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jan 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2; 2025: 4; 2026: 1.
9 papers.
- 2024EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learningcited by 8
- 2021Toward AI Assistants That Let Designers Designcited by 6
- 2024Graph4GUI: Graph Neural Networks for Representing Graphical User Interfacescited by 6
- 2025From Fragment to One Piece: A Survey on AI-Driven Graphic Designcited by 2
- 2022Cross-Domain Document Layout Analysis Using Document Style Guidecited by 1
- 2025Adaptive 3D UI Placement in Mixed Reality Using Deep Reinforcement Learningcited by 1
- 2026Evolving Thematic Map Design in Academic Cartography: A Thirty-Year Study Based on Multilingual Journalscited by 1
- 2025StructLayoutFormer:Conditional Structured Layout Generation via Structure Serialization and Disentanglementcited by 0
- 2025LAPPI: Interactive Optimization with LLM-Assisted Preference-Based Problem Instantiationcited by 0
- 2026Evolving Thematic Map Design in Academic Cartography: A Thirty-Year Study Based on Multilingual Journals24 Apr 2026
- 2025LAPPI: Interactive Optimization with LLM-Assisted Preference-Based Problem Instantiation16 Dec 2025
- 2025StructLayoutFormer:Conditional Structured Layout Generation via Structure Serialization and Disentanglement30 Oct 2025
- 2025Adaptive 3D UI Placement in Mixed Reality Using Deep Reinforcement Learning30 Apr 2025
- 2025From Fragment to One Piece: A Survey on AI-Driven Graphic Design24 Mar 2025
- 2024Graph4GUI: Graph Neural Networks for Representing Graphical User Interfaces21 Apr 2024
- 2024EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning15 Apr 2024
- 2022Cross-Domain Document Layout Analysis Using Document Style Guide24 Jan 2022
- 2021Toward AI Assistants That Let Designers Design22 Jul 2021