目的 在人工智能赋能非遗文化传承的背景下,针对传统广绣技艺创作耗时长、难度大、迭代成本高等问题,探索大众认识并体验广绣文化的新路径。方法 首先,从AIGC技术特征出发,分析其对广绣进行设计转化的可行性。其次,以广绣作品为切入点,对其艺术特征与文化内涵进行提取与分析,转化为AI生成广绣的描述词。最后,运用Flux训练LoRA模型,并搭建适用于AI生成广绣的工作流。结果 基于AI生成广绣技术,构建了由广绣文化特征体验与广绣创作两个部分组成的体验系统,结合用户需求进行了设计实践和相关效果验证。结论 AIGC引导用户与广绣深度接触,改善用户参与广绣制作体验。用户在与AI的互动中能够感受广绣文化内核,同时在AI的反馈下激发创意突破,有利于广绣在当代的有效表达与活化。
Abstract
In the context of artificial intelligence empowering intangible cultural heritage, the work aims to explore new pathways for the public to understand and experience Guang embroidery culture by addressing the challenges of traditional Guang embroidery, such as time-consuming processes, high difficulty, and costly iterations. Firstly, drawing on the characteristics of AIGC, the feasibility of design transformation for Guang embroidery was analyzed. Secondly, the artistic features and cultural connotations of Guang embroidery works were extracted and translated into descriptive prompts for AI generation. Finally, a LoRA model was trained with Flux and integrated into a workflow for AI-based Guang embroidery creation. Based on generative AI technology for Guang embroidery, an experiential system was constructed, comprising two modules of cultural feature exploration and embroidery creation. Furthermore, design implementation and effectiveness validation were conducted in alignment with user needs. AIGC guides users to engage deeply with Guang embroidery, enhancing their experience of participating in its creation. Through interaction with AI, users can perceive the cultural essence of Guang embroidery, while AI feedback inspires creative breakthroughs, facilitating the effective contemporary expression and revitalization of Guang embroidery.
关键词
广绣技艺创作 /
人工智能生成内容(AIGC) /
人智协作 /
体验设计
Key words
Guang embroidery art creation /
artificial intelligence generated content (AIGC) /
human-AI collaboration /
experience design
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参考文献
[1] 缪丽泽. 迁移理论下的广绣艺术校园传承设计[D]. 广州: 广州大学, 2023: 1-2.
MIAO L Z.Design of Guang Embroidery Art Inheritance in Campus under the Theory of Transfer[D]. Guangzhou: Guangzhou University, 2023: 1-2.
[2] 李江敏, 王青, 朱镇. 非物质文化遗产活态传承: 体验价值体系、测量与检验[J]. 旅游学刊, 2020, 35(11): 78-89.
LI J M, WANG Q, ZHU Z.Living Transmission of Intangible Cultural Heritage: Experience Value System, Measurement and Test[J]. Tourism Tribune, 2020, 35(11): 78-89.
[3] 肖昕, 李沁怡. 基于峰终定律的长沙棕编非遗技艺体验数字化设计与实践[J]. 设计艺术研究, 2024, 14(5): 97-102.
XIAO X, LI Q Y.Digital Design and Practice of Changsha Palm Fibre Weaving Skill on the Basis of Peak- End Rule[J]. Design Research, 2024, 14(5): 97-102.
[4] 吴琼. 面向文化遗产的数字化体验设计[J]. 装饰, 2019(1): 12-15.
WU Q.Digital Experience Design for Cultural Heritage[J]. Art & Design, 2019(1): 12-15.
[5] 李白杨, 白云, 詹希旎, 等. 人工智能生成内容(AIGC)的技术特征与形态演进[J]. 图书情报知识, 2023, 40(1): 66-74.
LI B Y, BAI Y, ZHAN X N, et al.The Technical Features and Aromorphosis of Artificial Intelligence Generated Content(AIGC)[J]. Document, Informaiton & Knowledge, 2023, 40(1): 66-74.
[6] 杨小晖, 米高峰. AIGC设计赋能非物质文化遗产传承与创新[J]. 工业设计研究, 2023(1): 294-300.
YANG X H, MI G F.AIGC Design Empowers the Inheritance and Innovation of Intangible Cultural Heritage[J]. Industrial Design Research, 2023(1): 294-300.
[7] 周琪瑶, 赵卫东. 生成式人工智能辅助下文创设计方法研究综述[J]. 包装工程, 2025, 46(4): 121-133.
ZHOU Q Y, ZHAO W D.Review of Cultural and Creative Design Methods in the Context of Generative Artificial Intelligence Assistance[J]. Packaging Engineering, 2025, 46(4): 121-133.
[8] 李加, 张景. 美育视阈下的蜀绣AIGC创新设计与数字化推广研究[J]. 包装工程, 2024, 45(4): 485-490.
LI J, ZHANG J.Innovative Design and Digital Promotion of Shu Embroidery AIGC under the Threshold of Aesthetic Education[J]. Packaging Engineering, 2024, 45(4): 485-490.
[9] XU H R, CHEN S Y, YING Z.Magical Brush: A Symbol-Based Modern Chinese Painting System for Novices[C]//Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. Hamburg: ACM, 2023: 1-14.
[10] 徐晨雨, 祁悦, 郭瀚之, 等. 基于AIGC的西夏文化元素提炼及混合现实数字人互动设计[J]. 包装工程, 2024, 45(20): 49-57.
XU C Y, QI Y, GUO H Z, et al.AIGC-Based Western Xia Cultural Element Extraction and Mixed Reality Digital Human Interaction Design[J]. Packaging Engineering, 2024, 45(20): 49-57.
[11] ZHANG W, KAM-KWAI W, XU B Y, et al. CultiVerse: Towards Cross-Cultural Understanding for Paintings with Large Language Model[EB/OL].2024: arXiv: 2405. 00435. https://arxiv.org/abs/2405.00435
[12] 刘旭. 文化转译模型的构建及其对移动用户体验的影响[D]. 武汉: 华中科技大学, 2017.
LIU X.The Construction of Cultural Translation Model and Its Influence on the Mobile User Experience[D]. Wuhan: Huazhong University of Science and Technology, 2017.
[13] LEONG B D, CLARK H.Culture-Based Knowledge towards New Design Thinking and Practice—A Dialogue[J]. Design Issues, 2003, 19(3): 48-58.
[14] 陈贤昌. 岭南传统工艺广绣艺术作品题材分析[J]. 美术大观, 2012(12): 62.
CHEN X C.Theme Analysis of Lingnan Traditional Craft Guang Embroidery Works[J]. Art Panorama, 2012(12): 62.
[15] 张正义. 中国古代刺绣沿革及沈阳故宫藏明清刺绣藏品综述[J]. 浙江纺织服装职业技术学院学报, 2018, 17(3): 41-49.
ZHANG Z Y.On the Evolution of Embroidery in Ancient China and the Summary of the Collection of Embroidery in Ming and Qing Dynasties in Shenyang Palace Museum[J]. Journal of Zhejiang Fashion Institute of Technology, 2018, 17(3): 41-49.
[16] 罗洁, 廖煜容. 广绣与潮绣的艺术风格与工艺比较研究[J]. 装饰, 2022(1): 114-118.
LUO J, LIAO Y R.Comparative Study on Artistic Style and Craft of Guangdong Embroidery and Chaozhou Embroidery[J]. Art & Design, 2022(1): 114-118.
[17] 罗龙林, 马燕红. 基于CLO3D虚拟试衣技术的广绣纹样在女西装设计中的应用与实践[J]. 纺织科技进展, 2023, 45(10): 40-47.
LUO L L, MA Y H.Research on the Design Method of Canton Embroidery Pattern in Women's Suit Based on CLO3D Virtual Fitting Technology[J]. Progress in Textile Science & Technology, 2023, 45(10): 40-47.
[18] 侯云鹏, 彭涵, 刘育晖. 基于LoRA模型的非遗数字化传承: 以楚漆器为例[J]. 设计艺术研究, 2024, 14(1): 14-18.
HOU Y P, PENG H, LIU Y H.Digital Inheritance of Intangible Cultural Heritage Based on the LoRA Model: A Case Study of Chu Lacquerware[J]. Design Research, 2024, 14(1): 14-18.
[19] 吴海鸣, 陈敬玉. 基于AIGC技术的民族服饰设计研究——以畲族为例[J]. 丝绸, 2025, 62(1): 20-29.
WU H M, CHEN J Y.Research on Ethnic Costume Design Based on AIGC Technology: Taking the She Ethnic Group as an Example[J]. Silk, 2025, 62(1): 20-29.
[20] CHEN J S, WU Y, LUO S M, et al. PIXART-Δ: Fast and Controllable Image Generation with Latent Consistency Models[EB/OL].2024: arXiv: 2401.05252. https://arxiv.org/abs/2401.05252
[21] 刘可妮. 从《黑神话·悟空》看中华优秀传统文化的对外传播[J]. 声屏世界, 2025(14): 92-95.
LIU K N.On the External Dissemination of Chinese Excellent Traditional Culture from Black Myth Wukong[J]. Voice & Screen World, 2025(14): 92-95.
[22] 钱琰彬. 新文创视域下博物馆文创产品设计研究[D]. 无锡: 江南大学, 2021.
QIAN Y B.Research on the Design of Museum Cultural and Creative Products from the Perspective of New Cultural and Creative[D]. Wuxi: Jiangnan University, 2021.
[23] 詹希旎, 李白杨, 孙建军. 数智融合环境下AIGC的场景化应用与发展机遇[J]. 图书情报知识, 2023, 40(1): 75-85.
ZHAN X N, LI B Y, SUN J J.Application Scenarios and Development Opportunities of AIGC in the Digital Intelligence Integration Environment[J]. Document, Informaiton & Knowledge, 2023, 40(1): 75-85.
[24] 李杰, 王路平. 大模型时代的AIGC设计新范式[J]. 设计, 2024, 37(2): 76-82.
LI J, WANG L P.The New Paradigm of Aigc Design in the Era of Large Models[J]. Design, 2024, 37(2): 76-82.
基金
广东省哲学社会科学规划2023年度学科共建项目(GD23XYS066); 2020年度教育部人文社会科学研究规划基金(20YJA760008); 广州市哲学社科规划2022年度课题(2022GZGJ279)