Preserving Conservation Cultural Materials with AI: A Human Pose Estimation Approach
Zhijiang Lan
International Journal of Web Applications, Vol. 17, No. 4, 2025.
DOI: https://doi.org/10.6025/ijwa/2025/17/4/163-170
Link: https://www.dline.info/ijwa/fulltext/v17n4/ijwav17n4_3.pdf
Abstract:
This paper explores how deep learning can be leveraged to preserve and promote traditional Chinese martial arts, which are a vital part of Chinaâ€™s intangible cultural heritage. Historically transmitted through master apprentice relationships, these arts now face challenges due to societal modernization and demographic shifts. The author proposes using deep learning models particularly convolutional neural networks (CNNs) to capture, analyze, and reconstruct martial arts movements through human pose estimation. A novel model, IPN (Involution Pose Estimation Net), built on Simple Baselines and Involution mechanisms, is introduced to identify key body joints from video data with high accuracy. The study utilizes datasets like NTURGB+D and UTD-MHAD, though it acknowledges their limitations for martial arts specific actions, highlighting the need for a dedicated Chinese martial arts motion database. Evaluation metrics such as tMPJPE (time aware Mean Per Joint Position Error) are adapted to assess pose accuracy over time. Experimental results demonstrate effective movement recognition in â€œYouth Serial Boxing,â€ with promising convergence and accuracy. The research underscores deep learningâ€™s potential not only in digitizing martial arts but also in enabling broader cultural preservation in the intelligent era. Future work includes expanding the model to other martial arts styles and improving dataset specificity to enhance training and generalization.