ScreenCoder: Advancing Visual-to-Code Generation for Front-End
  Automation via Modular Multimodal Agents
Authors: name: Yilei Jiang; name: Yaozhi Zheng; name: Yuxuan Wan; name: Jiaming Han; name: Qunzhong Wang; name: Michael R. Lyu; name: Xiangyu Yue
Published: 2025-07-30 16:41:21

Abstract: Automating the transformation of user interface (UI) designs into front-end
code holds significant promise for accelerating software development and
democratizing design workflows. While recent large language models (LLMs) have
demonstrated progress in text-to-code generation, many existing approaches rely
solely on natural language prompts, limiting their effectiveness in capturing
spatial layout and visual design intent. In contrast, UI development in
practice is inherently multimodal, often starting from visual sketches or
mockups. To address this gap, we introduce a modular multi-agent framework that
performs UI-to-code generation in three interpretable stages: grounding,
planning, and generation. The grounding agent uses a vision-language model to
detect and label UI components, the planning agent constructs a hierarchical
layout using front-end engineering priors, and the generation agent produces
HTML/CSS code via adaptive prompt-based synthesis. This design improves
robustness, interpretability, and fidelity over end-to-end black-box methods.
Furthermore, we extend the framework into a scalable data engine that
automatically produces large-scale image-code pairs. Using these synthetic
examples, we fine-tune and reinforce an open-source VLM, yielding notable gains
in UI understanding and code quality. Extensive experiments demonstrate that
our approach achieves state-of-the-art performance in layout accuracy,
structural coherence, and code correctness. Our code is made publicly available
at https://github.com/leigest519/ScreenCoder.