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Related Experiment Video

Updated: Jan 16, 2026

A Web Tool for Generating High Quality Machine-readable Biological Pathways
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Multi-agent collaborative pathways for Chinese traditional architectural image generation.

Yi Lu1, Wenmiao Yuan1, Mengyao Wang2

  • 1School of Art and Design, Beijing University of Technology, Beijing, 100124, China.

Scientific Reports
|October 3, 2025
PubMed
Summary

This study introduces a multi-agent framework for generating images of Chinese traditional architecture from vague text descriptions. The AI system enhances cultural accuracy and visual fidelity for digital heritage and tourism.

Keywords:
Chinese traditional architectureDigital cultural tourismGenerative artificial intelligenceMulti-agent collaboration

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Digital Heritage

Background:

  • Artificial Intelligence Generated Content (AIGC) shows promise for cultural heritage digitalization and tourism design.
  • Existing AIGC struggles with the nuanced cultural and visual complexity of Chinese traditional architecture, particularly with vague user inputs.
  • Limitations include understanding user intent, ensuring cultural accuracy, and generating diverse, high-fidelity images.

Purpose of the Study:

  • To propose a novel text-to-image generation framework specifically for Chinese traditional architecture.
  • To address the limitations of current AIGC in handling cultural specificity and vague user requirements.
  • To enhance the digital preservation, creative transformation, and intelligent design of cultural heritage.

Main Methods:

  • Development of a multi-agent collaborative framework for text-to-image generation.
  • Integration of agents for user intent understanding, prompt generation, image creation, and cultural assessment.
  • Construction of a Chinese Traditional Architecture Cultural Knowledge Base (e.g., Beijing Central Axis).
  • Design of a collaborative workflow for agent interaction and task scheduling.

Main Results:

  • The proposed framework effectively transforms colloquial and vague descriptions into culturally rich, high-fidelity images of traditional Chinese architecture.
  • Experimental results show significant improvements over baseline models in handling vague inputs and ensuring cultural accuracy.
  • The framework demonstrates enhanced user intent matching and greater creative diversity in generated images.

Conclusions:

  • The multi-agent framework offers a viable solution for AI-driven generation of culturally specific architectural imagery.
  • This research provides new theoretical and practical approaches for AI in cultural heritage digitalization and creative design.
  • The framework serves as a valuable tool for innovative digital cultural tourism experiences and cultural inheritance.