Reconstructing the Cultural Genes and Innovative Design of Qing Dynasty Embroidery Patterns: A Case Study of Official Rank Badges

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This paper studied how to digitally reconstruct and generate Qing Dynasty embroidered official rank badges (Guangbu) by extracting pattern “cultural genes” and controlling modern pattern forms, using multi-source inputs including images, atlases, and literature. The authors built an “embroidery semantic network” for structured representation of cultural elements and introduced “dynamic shape grammar,” then combined these with a three-stage LoRA-Diffusion-SG fine-tuning pipeline to improve realism and cultural alignment of generated patterns. Experiments reported cultural compliance measured by CLIP similarity (0.78–0.82), symbol recognition accuracy (Top-1 82–85%), and craft-feasibility prediction (F1-score 0.87–0.89), with no explicit competing interests stated. The study is a preprint and the work is characterized as not peer reviewed by a journal. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract As an important carrier of traditional Chinese culture, Qing Dynasty embroidered rank badges (Guangbu) hold rich cultural genes and historical information within their patterns. Currently, in the research and innovative design of traditional patterns, there are still multiple challenges, including digital expression, cultural gene extraction, and modern application. This paper, for the first time, constructs an 'embroidery semantic network' for Qing Dynasty embroidered rank badge patterns, achieving structured expression of pattern cultural elements.It also innovatively introduces 'dynamic shape grammar' to achieve precise control over pattern forms. Combined with a three-stage fine-tuning architecture of LoRA-Diffusion-SG, it significantly enhances the realism and cultural alignment of generated patterns. The research integrates multi-source data such as images, atlases, and literature, establishing a comprehensive dataset comprising image, knowledge, and craftsmanship layers. Experiments show that the proposed method performs excellently in terms of cultural compliance (CLIP similarity 0.78–0.82), symbol recognition accuracy (Top-1 82%–85%), and craft feasibility prediction (F1-score 0.87–0.89). his study provides a theoretical basis and technical support for the digital preservation and redesign of embroidery patterns, promoting the deep application of artificial intelligence in cultural heritage inheritance.
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Reconstructing the Cultural Genes and Innovative Design of Qing Dynasty Embroidery Patterns: A Case Study of Official Rank Badges | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Reconstructing the Cultural Genes and Innovative Design of Qing Dynasty Embroidery Patterns: A Case Study of Official Rank Badges Haiqiong Yang, Maoning Li, Qiao Sui, Bing Hu, Kun Shi, Ranran Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7522369/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Dec, 2025 Read the published version in npj Heritage Science → Version 1 posted 9 You are reading this latest preprint version Abstract As an important carrier of traditional Chinese culture, Qing Dynasty embroidered rank badges (Guangbu) hold rich cultural genes and historical information within their patterns. Currently, in the research and innovative design of traditional patterns, there are still multiple challenges, including digital expression, cultural gene extraction, and modern application. This paper, for the first time, constructs an 'embroidery semantic network' for Qing Dynasty embroidered rank badge patterns, achieving structured expression of pattern cultural elements.It also innovatively introduces 'dynamic shape grammar' to achieve precise control over pattern forms. Combined with a three-stage fine-tuning architecture of LoRA-Diffusion-SG, it significantly enhances the realism and cultural alignment of generated patterns. The research integrates multi-source data such as images, atlases, and literature, establishing a comprehensive dataset comprising image, knowledge, and craftsmanship layers. Experiments show that the proposed method performs excellently in terms of cultural compliance (CLIP similarity 0.78–0.82), symbol recognition accuracy (Top-1 82%–85%), and craft feasibility prediction (F1-score 0.87–0.89). his study provides a theoretical basis and technical support for the digital preservation and redesign of embroidery patterns, promoting the deep application of artificial intelligence in cultural heritage inheritance. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 Dec, 2025 Read the published version in npj Heritage Science → Version 1 posted Editorial decision: Revision requested 08 Nov, 2025 Reviews received at journal 04 Nov, 2025 Reviews received at journal 02 Nov, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers invited by journal 12 Oct, 2025 Editor assigned by journal 18 Sep, 2025 Submission checks completed at journal 03 Sep, 2025 First submitted to journal 02 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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