The rapid progress of Artificial Intelligence Generated Content (AIGC) tools enables images, videos, and visualizations to be created on demand for webpage design, offering a flexible and increasingly adopted paradigm for modern UI/UX. However, directly integrating such tools into automated webpage generation often leads to style inconsistency and poor global coherence, as elements are generated in isolation. We propose MM-WebAgent, a hierarchical agentic framework for multimodal webpage generation that coordinates AIGC-based element generation through hierarchical planning and iterative self-reflection. MM-WebAgent jointly optimizes global layout, local multimodal content, and their integration, producing coherent and visually consistent webpages. We further introduce a benchmark for multimodal webpage generation and a multi-level evaluation protocol for systematic assessment. Experiments demonstrate that MM-WebAgent outperforms code-generation and agent-based baselines, especially on multimodal element generation and integration. Code & Data: https://aka.ms/mm-webagent.

翻译：人工智能生成内容（AIGC）工具的快速发展使得能够按需生成图像、视频及可视化内容用于网页设计，为现代UI/UX提供了一种灵活且日益普及的范式。然而，将这些工具直接集成到自动化网页生成中往往会导致风格不一致和全局连贯性差，因为各元素是孤立生成的。我们提出MM-WebAgent——一种分层代理框架，通过分层规划和迭代自我反思协调基于AIGC的元素生成，实现多模态网页生成。MM-WebAgent联合优化全局布局、局部多模态内容及其集成，生成连贯且视觉一致的网页。我们进一步引入了多模态网页生成基准测试及多层级评估协议用于系统评估。实验表明，MM-WebAgent在代码生成和基于代理的基线方法中表现更优，尤其在多模态元素生成与集成方面。代码与数据：https://aka.ms/mm-webagent。