Prototype2Code: End-to-end Front-end Code Generation from UI Design Prototypes
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Abstract (arXiv)
UI-to-code technology has streamlined the front-end development process, reducing repetitive tasks for engineers. prior research mainly use design prototypes as inputs, with the effectiveness of the generated code heavily dependent on these prototypes' quality, leading to compromised robustness. Moreover, these approaches also exhibit shortcomings in code quality, including issues such as disorganized UI structures and the inability to support responsive layouts. To address these challenges, we introduce Prototype2Code, which achieves end-to-end front-end code generation with business demands. For Prototype2Code, we incorporate design linting into the workflow, addressing the detection of fragmented elements and perceptual groups, enhancing the robustness of the generated outcomes. By optimizing the hierarchical structure and intelligently recognizing UI element types, Prototype2Code generates code that is more readable and structurally clearer. To meet responsive design requirements, Prototype2Code primarily supports flexbox layout model, ensuring code compatibility across various device sizes. To validate the efficacy, we compare Prototype2Code with the commercial code generation platform CodeFun and Screenshot-to-code based on GPT-4 with vision. Employing structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and mean squared error (MSE) for visual similarity assessment, Prototype2Code's rendered UI effects align most closely with the design prototypes, exhibiting the minimal errors. We also conduct a user study with five experienced front-end engineers, inviting them to review and revise code generated by the three methods. As a result, Prototype2Code surpasses other methods in readability, usability, and maintainability, better meeting the business needs of industrial development.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 May 2024.
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Citing papers per year: 2024: 1; 2025: 6; 2026: 3.
10 papers.
- 2025P2P: Automated Paper-to-Poster Generation and Fine-Grained Benchmarkcited by 17
- 2026VisRefiner: Learning from Visual Differences for Screenshot-to-Code Generationcited by 9
- 2025Improving Existing Optimization Algorithms with LLMscited by 4
- 2025From PowerPoint UI Sketches to Web-Based Applications: Pattern-Driven Code Generation for GIS Dashboard Development Using Knowledge-Augmented LLMs, Context-Aware Visual Prompting, and the React Frameworkcited by 1
- 2025Paper2Web: Let's Make Your Paper Alive!cited by 1
- 2024Fragmented Layer Grouping in GUI Designs Through Graph Learning Based on Multimodal Informationcited by 0
- 2025Context-Aware Visual Prompting: Automating Geospatial Web Dashboards with Large Language Models and Agent Self-Validation for Decision Supportcited by 0
- 2025Beyond Prototyping: Autonomous, Enterprise-Grade Frontend Development from Pixel to Production via a Specialized Multi-Agent Frameworkcited by 0
- 2026From Mirage to Grounding: Towards Reliable Multimodal Circuit-to-Verilog Code Generationcited by 0
- 2026Decomposer: Learning to Decompile Symbolic Music to Programscited by 0
- 2026Decomposer: Learning to Decompile Symbolic Music to Programs2 Jul 2026
- 2026From Mirage to Grounding: Towards Reliable Multimodal Circuit-to-Verilog Code Generation30 Apr 2026
- 2026VisRefiner: Learning from Visual Differences for Screenshot-to-Code Generation5 Feb 2026
- 2025Beyond Prototyping: Autonomous, Enterprise-Grade Frontend Development from Pixel to Production via a Specialized Multi-Agent Framework5 Dec 2025
- 2025Paper2Web: Let's Make Your Paper Alive!17 Oct 2025
- 2025Context-Aware Visual Prompting: Automating Geospatial Web Dashboards with Large Language Models and Agent Self-Validation for Decision Support10 Oct 2025
- 2025P2P: Automated Paper-to-Poster Generation and Fine-Grained Benchmark21 May 2025
- 2025From PowerPoint UI Sketches to Web-Based Applications: Pattern-Driven Code Generation for GIS Dashboard Development Using Knowledge-Augmented LLMs, Context-Aware Visual Prompting, and the React Framework12 Feb 2025
- 2025Improving Existing Optimization Algorithms with LLMs12 Feb 2025
- 2024Fragmented Layer Grouping in GUI Designs Through Graph Learning Based on Multimodal Information7 Dec 2024