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<p>To overcome the limitations of Artificial Intelligence (AI) in the field of architectural design, particularly regarding issues of interoperability, domain-specific knowledge and interdisciplinary, we propose an innovative multimodal AI platform—FUGenerator. Within this framework, we designed a multimodal knowledge graph, a multimodal algorithm library and a traceable workflow. Additionally, by implementing advanced AI technologies such as Natural Language Processing (NLP), image processing, and 3D model generation, the platform is capable of processing diverse formats of input data and generating preliminary design proposals that are accurate and personalized design solutions. During the application experiment, students used the platform in their fourth-year undergraduate design projects. The outcomes demonstrated its effectiveness in not only generating diverse design alternatives based on various design requirements, but also significantly improving design efficiency and flexibility in different scenarios. With further optimization and expansion, the platform can become the multimodal intelligent support tool throughout the entire design and construction process, driving the digitalization and intelligent evolution of architectural practice.</p>

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FUGenerator: multimodal-AI platform for architectural design

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月22日

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  • Xinhui Xu,
  • Tinghao Feng,
  • Yulu Zhang,
  • Zhengcheng He,
  • Philip F. Yuan

摘要

To overcome the limitations of Artificial Intelligence (AI) in the field of architectural design, particularly regarding issues of interoperability, domain-specific knowledge and interdisciplinary, we propose an innovative multimodal AI platform—FUGenerator. Within this framework, we designed a multimodal knowledge graph, a multimodal algorithm library and a traceable workflow. Additionally, by implementing advanced AI technologies such as Natural Language Processing (NLP), image processing, and 3D model generation, the platform is capable of processing diverse formats of input data and generating preliminary design proposals that are accurate and personalized design solutions. During the application experiment, students used the platform in their fourth-year undergraduate design projects. The outcomes demonstrated its effectiveness in not only generating diverse design alternatives based on various design requirements, but also significantly improving design efficiency and flexibility in different scenarios. With further optimization and expansion, the platform can become the multimodal intelligent support tool throughout the entire design and construction process, driving the digitalization and intelligent evolution of architectural practice.