Research on Multi‐Feature Fusion Shadow Puppet Motifs Generation Based on CSPMotifsGAN and Cultural Heritage Preservation
ABSTRACT As quintessential cultural symbols in traditional shadow puppetry, artistic motifs encapsulate profound historical narratives and serve as vital conduits for intangible cultural heritage preservation. However, this craft confronts existential threats from digital entertainment proliferation...
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| Published in | Computer animation and virtual worlds Vol. 36; no. 3 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, USA
John Wiley & Sons, Inc
01.05.2025
Wiley Subscription Services, Inc |
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| Abstract | ABSTRACT
As quintessential cultural symbols in traditional shadow puppetry, artistic motifs encapsulate profound historical narratives and serve as vital conduits for intangible cultural heritage preservation. However, this craft confronts existential threats from digital entertainment proliferation and practitioner attrition. To address these challenges, this study proposes CSPMotifsGAN, an enhanced CycleGAN framework for constructing a motif data set through three‐stage processing: adaptive denoising, hierarchical classification, and multi‐branch feature extraction (contour, texture, color). By integrating adversarial loss, cycle‐consistency loss, and identity preservation loss, the model effectively resolves color distortion and textural degradation inherent in conventional CycleGAN. Experimental results demonstrate significant improvements: Fréchet Inception Distance (FID), Peak Signal‐to‐Noise Ratio (PSNR), and Structural Similarity Index (SSIM), validated through both subjective evaluations and statistical analysis.
This study proposes CSPMotifsGAN, enhancing CycleGAN with multi‐feature fusion (contour, texture, color). Experiments show superior performance over baselines, advancing digital preservation of shadow puppet heritage. |
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| AbstractList | As quintessential cultural symbols in traditional shadow puppetry, artistic motifs encapsulate profound historical narratives and serve as vital conduits for intangible cultural heritage preservation. However, this craft confronts existential threats from digital entertainment proliferation and practitioner attrition. To address these challenges, this study proposes CSPMotifsGAN, an enhanced CycleGAN framework for constructing a motif data set through three‐stage processing: adaptive denoising, hierarchical classification, and multi‐branch feature extraction (contour, texture, color). By integrating adversarial loss, cycle‐consistency loss, and identity preservation loss, the model effectively resolves color distortion and textural degradation inherent in conventional CycleGAN. Experimental results demonstrate significant improvements: Fréchet Inception Distance (FID), Peak Signal‐to‐Noise Ratio (PSNR), and Structural Similarity Index (SSIM), validated through both subjective evaluations and statistical analysis. ABSTRACT As quintessential cultural symbols in traditional shadow puppetry, artistic motifs encapsulate profound historical narratives and serve as vital conduits for intangible cultural heritage preservation. However, this craft confronts existential threats from digital entertainment proliferation and practitioner attrition. To address these challenges, this study proposes CSPMotifsGAN, an enhanced CycleGAN framework for constructing a motif data set through three‐stage processing: adaptive denoising, hierarchical classification, and multi‐branch feature extraction (contour, texture, color). By integrating adversarial loss, cycle‐consistency loss, and identity preservation loss, the model effectively resolves color distortion and textural degradation inherent in conventional CycleGAN. Experimental results demonstrate significant improvements: Fréchet Inception Distance (FID), Peak Signal‐to‐Noise Ratio (PSNR), and Structural Similarity Index (SSIM), validated through both subjective evaluations and statistical analysis. This study proposes CSPMotifsGAN, enhancing CycleGAN with multi‐feature fusion (contour, texture, color). Experiments show superior performance over baselines, advancing digital preservation of shadow puppet heritage. |
| Author | Liang, Hui Wang, Rui |
| Author_xml | – sequence: 1 givenname: Hui surname: Liang fullname: Liang, Hui email: hliang@zzuli.edu.cn organization: Zhengzhou University of Light Industry – sequence: 2 givenname: Rui orcidid: 0009-0004-9720-7637 surname: Wang fullname: Wang, Rui organization: Zhengzhou University of Light Industry |
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| Notes | Funding This work was supported by Research Project of Humanities and Social Sciences of the Ministy of Education (24YJAZH075), Research Project of Humanities and Social Sciences of Henan Province (2025‐ZZJH‐370), Research Project of Intangible Cultural Heritage of Henan Province, (24HNFY‐LX149), Postgraduate Education Reform and Quality Improvement Project of Henan Province (YJS2025AL39), International Cooperation Project of Henan Province (252102520012). ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
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As quintessential cultural symbols in traditional shadow puppetry, artistic motifs encapsulate profound historical narratives and serve as vital... As quintessential cultural symbols in traditional shadow puppetry, artistic motifs encapsulate profound historical narratives and serve as vital conduits for... |
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| SubjectTerms | Color CSPMotifsGAN cultural heritage Cultural resources CycleGAN Feature extraction motif generation Shadows Statistical analysis |
| Title | Research on Multi‐Feature Fusion Shadow Puppet Motifs Generation Based on CSPMotifsGAN and Cultural Heritage Preservation |
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