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Generative Large Model-Driven Methodology for Color Matching and Shape Design in IP Products.

Fan Wu1, Peng Lu2, Shih-Wen Hsiao3

  • 1Department of Product Design, Dalian Polytechnic University, Dalian 116034, China.

Entropy (Basel, Switzerland)
|March 28, 2025
PubMed
Summary

This study introduces a new AI-driven design method for tourism cultural products. It uses generative large models to create appealing shapes and colors, enhancing sustainable tourism development.

Keywords:
color matchingproduct designquadratic curvature entropyshape designtourism IP product

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Area of Science:

  • Design Science
  • Artificial Intelligence
  • Sustainable Tourism

Background:

  • Generative large models and AI-generated content (AIGC) are transforming product design.
  • Tourism intellectual property (IP) cultural products are vital for sustainable tourism.
  • Existing design methodologies lack integration of generative large models for tourism IP products.

Purpose of the Study:

  • To propose a practical methodology for color matching and shape design of tourism IP cultural products.
  • To leverage multimodal generative large models in the design process.
  • To enhance the appeal and effectiveness of tourism cultural products.

Main Methods:

  • Utilized GPT-4o for exploring visitor emotional needs and identifying target imagery.
  • Employed Midjourney for shape generation and quadratic curvature entropy for shape selection.
  • Used Midjourney for color image generation, AHP and OpenCV for color selection, and color harmony calculations for combination.
  • Conducted quantitative and qualitative evaluations using aesthetic measurement formulas and sensibility questionnaires.

Main Results:

  • A novel methodology integrating multimodal generative large models for tourism IP product design was developed.
  • The process effectively identified visitor emotional needs, generated suitable shapes, and selected optimal color combinations.
  • Case study on harbor seal IP products demonstrated strong correlation between quantitative and qualitative evaluations.

Conclusions:

  • The proposed methodology is effective for the color matching and shape design of tourism IP cultural products.
  • This approach enhances sustainable tourism development through innovative product design.
  • The integration of AI in cultural product design offers significant potential for the tourism industry.