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<p>Traditional Yi ethnic patterns, as vital cultural heritage, increasingly face marginalization in modern design contexts due to regional cultural specificity and shifting esthetic demands. This study pioneers a GAN-driven framework to digitally preserve and innovatively regenerate these patterns. A hybrid approach combining manual curation with computational preprocessing (e.g., eigenvector analysis, semantic annotation) established a structured database of Yi motifs. Leveraging this repository, a customized adversarial generative network was developed to synthesize culturally authentic yet stylistically adaptive patterns. Rigorous evaluations confirmed the model’s capacity to balance ethnic identity retention with creative diversification. The generated patterns demonstrate practical applicability in textile, architectural ornamentation, and digital media, bridging heritage conservation with contemporary design needs. By integrating database construction, generative modeling, and application-oriented validation, this work provides a scalable paradigm for revitalizing endangered traditional patterns across ethnic communities, offering both technical methodologies and cross-disciplinary insights for cultural sustainability.</p>

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Database construction and remodeling method on traditional Yi nationality patterns of China with GAN model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月17日

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  • Kaining Meng,
  • Hengyi Li,
  • Yunwei Shu,
  • Mengmeng Chen,
  • Xiao Han,
  • Lu He

摘要

Traditional Yi ethnic patterns, as vital cultural heritage, increasingly face marginalization in modern design contexts due to regional cultural specificity and shifting esthetic demands. This study pioneers a GAN-driven framework to digitally preserve and innovatively regenerate these patterns. A hybrid approach combining manual curation with computational preprocessing (e.g., eigenvector analysis, semantic annotation) established a structured database of Yi motifs. Leveraging this repository, a customized adversarial generative network was developed to synthesize culturally authentic yet stylistically adaptive patterns. Rigorous evaluations confirmed the model’s capacity to balance ethnic identity retention with creative diversification. The generated patterns demonstrate practical applicability in textile, architectural ornamentation, and digital media, bridging heritage conservation with contemporary design needs. By integrating database construction, generative modeling, and application-oriented validation, this work provides a scalable paradigm for revitalizing endangered traditional patterns across ethnic communities, offering both technical methodologies and cross-disciplinary insights for cultural sustainability.