Creating User Interface Mock-ups from High-Level Text Descriptions with Deep-Learning Models
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Abstract (arXiv)
The design process of user interfaces (UIs) often begins with articulating high-level design goals. Translating these high-level design goals into concrete design mock-ups, however, requires extensive effort and UI design expertise. To facilitate this process for app designers and developers, we introduce three deep-learning techniques to create low-fidelity UI mock-ups from a natural language phrase that describes the high-level design goal (e.g. "pop up displaying an image and other options"). In particular, we contribute two retrieval-based methods and one generative method, as well as pre-processing and post-processing techniques to ensure the quality of the created UI mock-ups. We quantitatively and qualitatively compare and contrast each method's ability in suggesting coherent, diverse and relevant UI design mock-ups. We further evaluate these methods with 15 professional UI designers and practitioners to understand each method's advantages and disadvantages. The designers responded positively to the potential of these methods for assisting the design process.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 15 of its 27 references that are in the map. First posted on arXiv 14 Oct 2021.
Builds on
15 of 27 references.
References · 15
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Built on by
Citing papers per year: 2023: 3; 2024: 1; 2025: 3; 2026: 1.
8 papers.
- 2023LayoutPrompter: Awaken the Design Ability of Large Language Modelscited by 48
- 2023A Parse-Then-Place Approach for Generating Graphic Layouts from Textual Descriptionscited by 15
- 2023UI Layout Generation with LLMs Guided by UI Grammarcited by 15
- 2025LayoutCoT: Unleashing the Deep Reasoning Potential of Large Language Models for Layout Generationcited by 7
- 2024Towards Educator-Driven Tutor Authoring: Generative AI Approaches for Creating Intelligent Tutor Interfacescited by 5
- 2026CoGen: Creation of Reusable UI Components in Figma via Textual Commandscited by 2
- 2025SheetDesigner: MLLM-Powered Spreadsheet Layout Generation with Rule-Based and Vision-Based Reflectioncited by 1
- 2025Feasibility of AI-Assisted Programming for End-User Developmentcited by 0
- 2026CoGen: Creation of Reusable UI Components in Figma via Textual Commands15 Jan 2026
- 2025Feasibility of AI-Assisted Programming for End-User Development5 Dec 2025
- 2025SheetDesigner: MLLM-Powered Spreadsheet Layout Generation with Rule-Based and Vision-Based Reflection9 Sep 2025
- 2025LayoutCoT: Unleashing the Deep Reasoning Potential of Large Language Models for Layout Generation15 Apr 2025
- 2024Towards Educator-Driven Tutor Authoring: Generative AI Approaches for Creating Intelligent Tutor Interfaces23 May 2024
- 2023LayoutPrompter: Awaken the Design Ability of Large Language Models11 Nov 2023
- 2023UI Layout Generation with LLMs Guided by UI Grammar24 Oct 2023
- 2023A Parse-Then-Place Approach for Generating Graphic Layouts from Textual Descriptions24 Aug 2023