@inproceedings{yue-etal-2025-uiorchestra,
title = "{UIO}rchestra: Generating High-Fidelity Code from {UI} Designs with a Multi-agent System",
author = "Yue, Chuhuai and
Chai, Jiajun and
Zhang, Yufei and
Ding, Zixiang and
Liang, Xihao and
Wang, Peixin and
Chen, Shihai and
Yixuan, Wang and
Wangyanping and
Yin, Guojun and
Lin, Wei",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.150/",
pages = "2769--2782",
ISBN = "979-8-89176-335-7",
abstract = "Recent advances in large language models (LLMs) have significantly improved automated code generation, enabling tools such as GitHub Copilot and CodeWhisperer to assist developers in a wide range of programming tasks. However, the translation of complex mobile UI designs into high-fidelity front-end code remains a challenging and underexplored area, especially as modern app interfaces become increasingly intricate. In this work, we propose UIOrchestra, a collaborative multi-agent system designed for the AppUI2Code task, which aims to reconstruct static single-page applications from design mockups. UIOrchestra integrates three specialized agents, layout description, code generation, and difference analysis agent that work collaboratively to address the limitations of single-model approaches. To facilitate robust evaluation, we introduce APPUI, the first benchmark dataset for AppUI2Code, constructed through a human-in-the-loop process to ensure data quality and coverage. Experimental results demonstrate that UIOrchestra outperforms existing methods in reconstructing complex app pages and highlight the necessity of multi-agent collaboration for this task. We hope our work will inspire further research on leveraging LLMs for front-end automation. The code and data will be released upon paper acceptance."
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<abstract>Recent advances in large language models (LLMs) have significantly improved automated code generation, enabling tools such as GitHub Copilot and CodeWhisperer to assist developers in a wide range of programming tasks. However, the translation of complex mobile UI designs into high-fidelity front-end code remains a challenging and underexplored area, especially as modern app interfaces become increasingly intricate. In this work, we propose UIOrchestra, a collaborative multi-agent system designed for the AppUI2Code task, which aims to reconstruct static single-page applications from design mockups. UIOrchestra integrates three specialized agents, layout description, code generation, and difference analysis agent that work collaboratively to address the limitations of single-model approaches. To facilitate robust evaluation, we introduce APPUI, the first benchmark dataset for AppUI2Code, constructed through a human-in-the-loop process to ensure data quality and coverage. Experimental results demonstrate that UIOrchestra outperforms existing methods in reconstructing complex app pages and highlight the necessity of multi-agent collaboration for this task. We hope our work will inspire further research on leveraging LLMs for front-end automation. The code and data will be released upon paper acceptance.</abstract>
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%0 Conference Proceedings
%T UIOrchestra: Generating High-Fidelity Code from UI Designs with a Multi-agent System
%A Yue, Chuhuai
%A Chai, Jiajun
%A Zhang, Yufei
%A Ding, Zixiang
%A Liang, Xihao
%A Wang, Peixin
%A Chen, Shihai
%A Yixuan, Wang
%A Yin, Guojun
%A Lin, Wei
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%A Wangyanping
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F yue-etal-2025-uiorchestra
%X Recent advances in large language models (LLMs) have significantly improved automated code generation, enabling tools such as GitHub Copilot and CodeWhisperer to assist developers in a wide range of programming tasks. However, the translation of complex mobile UI designs into high-fidelity front-end code remains a challenging and underexplored area, especially as modern app interfaces become increasingly intricate. In this work, we propose UIOrchestra, a collaborative multi-agent system designed for the AppUI2Code task, which aims to reconstruct static single-page applications from design mockups. UIOrchestra integrates three specialized agents, layout description, code generation, and difference analysis agent that work collaboratively to address the limitations of single-model approaches. To facilitate robust evaluation, we introduce APPUI, the first benchmark dataset for AppUI2Code, constructed through a human-in-the-loop process to ensure data quality and coverage. Experimental results demonstrate that UIOrchestra outperforms existing methods in reconstructing complex app pages and highlight the necessity of multi-agent collaboration for this task. We hope our work will inspire further research on leveraging LLMs for front-end automation. The code and data will be released upon paper acceptance.
%U https://aclanthology.org/2025.findings-emnlp.150/
%P 2769-2782
Markdown (Informal)
[UIOrchestra: Generating High-Fidelity Code from UI Designs with a Multi-agent System](https://aclanthology.org/2025.findings-emnlp.150/) (Yue et al., Findings 2025)
ACL
- Chuhuai Yue, Jiajun Chai, Yufei Zhang, Zixiang Ding, Xihao Liang, Peixin Wang, Shihai Chen, Wang Yixuan, Wangyanping, Guojun Yin, and Wei Lin. 2025. UIOrchestra: Generating High-Fidelity Code from UI Designs with a Multi-agent System. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 2769–2782, Suzhou, China. Association for Computational Linguistics.