AI-POWERED INSIGHTS
Research on the Design Method of Cloisonné Production Technique Based on Artificial Intelligence Diffusion Mode
This research introduces a novel AI-driven generative design method for cloisonné vase forms, addressing the challenges of traditional manual design in this intangible cultural heritage. By fine-tuning a diffusion model (CCPES-M) on a custom dataset of Chinese cloisonné vases (CCBS-D), the study demonstrates significant improvements in design efficiency and fidelity compared to mainstream AI models. This approach not only streamlines the design workflow for artisans but also promotes the sustainable preservation and innovation of cloisonné craftsmanship, offering a transferable model for other cultural heritage conservation efforts.
Executive Impact: Quantified Advantages
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Time Saved per Design (hours)
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Design Accuracy Increase
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Model Generation Speed (seconds)
Deep Analysis & Enterprise Applications
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The proposed method involves five key steps: data collection and preprocessing, dataset creation, diffusion model fine-tuning (resulting in CCPES-M), model evaluation and solidification, and model application for design generation. This structured approach aims to replace traditional hand-drawing with an efficient AI-driven process.
Generative Design Workflow for Cloisonné Vases
Data Collection & Preprocessing
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Dataset Creation (CCBS-D)
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Diffusion Model Fine-tuning (CCPES-M)
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Model Evaluation & Solidification
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Model Application for Design
Design Generation Time
2.3s Seconds to generate 8 design imagesA comprehensive mixed evaluation, involving 30 participants including 4 cloisonné inheritors and 6 professors, assessed the CCPES-M model against mainstream models (Stable Diffusion, Midjourney, DALL-E 3) across criteria like Vase Style, Pattern Symbolism, and Usability. CCPES-M consistently outperformed others.
| Model | Vase Style | Pattern Symbolism | Usability | Material Texture | Pattern Composition | Color Matching |
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| Stable Diffusion |
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| Midjourney |
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| DALL-E 3 |
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| CCPES-M |
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CCPES-M Outperformance
Top Performs across all evaluation metricsThis AI-driven approach significantly reduces design time and costs, supporting the sustainable preservation and innovation of cloisonné. The methodology is generalizable to other ICH requiring image creation, such as lacquerware and wood carving. Future work includes multimodal data integration and continuous improvement of assessment models.
Sustainable ICH Preservation
The CCPES-M model offers a vital tool for preserving and innovating intangible cultural heritage like cloisonné craftsmanship. By reducing the reliance on time-consuming manual processes and facilitating rapid design iteration, it ensures the craft remains vibrant and accessible for future generations. This directly addresses the challenge of younger artisans finding traditional methods too labor-intensive.
- Reduced design time from 5-10 days to 2.3 seconds.
- Enabled new creative combinations for vase shapes.
- Provides a framework for other ICH forms.
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