02519nas a2200373   4500000000100000008004100001653002000042653001600062653002700078653003400105653002600139653002100165653003200186653002000218653002600238653002700264653002300291653003400314653003400348653002200382653002300404653003200427653002300459653002400482653002900506653001500535653003200550653002000582100001600602245012400618856014400742490001000886520124900896                                       d10aComputer Vision10aConvolution10aConvolutional networks10aConvolutional neural networks10aCultural preservation10aDigital modeling10aGraph Convolutional Network10aGraphic methods10aHistoric preservation10aHuman pose estimations10aImage segmentation10aIntangible cultural heritages10aInteractive computer graphics10aMotion estimation10aMotion recognition10aPattern recognition systems10aRecognition models10aSimulation platform10aSkill motion recognition10aSmart City10aUrban cultural intelligence10aVirtual reality1 aXiwen Zhang00aSkill motion recognition and digital modeling of intangible cultural heritage for smart cities based on computer vision  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-105010641339&doi=10.1117%2F12.3073363&partnerID=40&md5=7e1eb0cfad3d5b7af10ef5a69f0bd22b0 v136823 aThe integration of cultural preservation into smart city initiatives has become increasingly vital as urban systems seek to balance technological advancement with heritage sustainability. This paper presents a computer vision-based framework for recognizing and digitally modeling the intricate skill motions involved in intangible cultural heritage (ICH), such as paper cutting, calligraphy, and embroidery. The system combines pose estimation, action segmentation, and graph-based temporal modeling to capture fine-grained spatial-temporal patterns of craft demonstrations. A dedicated ICH dataset, recorded from authentic workshops, is used to train a hybrid neural network architecture combining 2D CNNs with temporal graph convolutional networks. The recognized motion sequences are reconstructed into digital avatars for use in virtual exhibitions, educational platforms, and cultural simulation systems within smart city infrastructures. Experimental results demonstrate significant improvements over baseline models in recognition accuracy and motion fidelity. This framework provides a foundation for the intelligent integration of ICH into digital urban services, supporting long-term cultural dissemination in future smart societies.