TY - CPAPER
KW - Computer Vision
KW - Convolution
KW - Convolutional networks
KW - Convolutional neural networks
KW - Cultural preservation
KW - Digital modeling
KW - Graph Convolutional Network
KW - Graphic methods
KW - Historic preservation
KW - Human pose estimations
KW - Image segmentation
KW - Intangible cultural heritages
KW - Interactive computer graphics
KW - Motion estimation
KW - Motion recognition
KW - Pattern recognition systems
KW - Recognition models
KW - Simulation platform
KW - Skill motion recognition
KW - Smart City
KW - Urban cultural intelligence
KW - Virtual reality
AU - Xiwen Zhang
AB - The 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.
C2 - Proceedings of SPIE - The International Society for Optical Engineering
DO - 10.1117/12.3073363
N1 - Type: Conference paper
N2 - The 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.
TI - Skill motion recognition and digital modeling of intangible cultural heritage for smart cities based on computer vision
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-105010641339&doi=10.1117%2F12.3073363&partnerID=40&md5=7e1eb0cfad3d5b7af10ef5a69f0bd22b
VL - 13682
ER -