Research on Multi‐Feature Fusion Shadow Puppet Motifs Generation Based on CSPMotifsGAN and Cultural Heritage Preservation - Liang, Hui; Wang, Rui (Computer animation and virtual worlds, 2025)
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.