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Identification Model of Writhing Posture of Classical Dance Based on Motion Capture Technology and Few-Shot Learning

Chinese classical dance is cut into the inner verve from a grasp of external form in dance instruction, and the aesthetic fashion and artistic norms of classical dance are established with historical depth. The “professional specificity” of characters and the “language description” of plots are elim...

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Autor principal: Zhang, Ning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9113880/
https://www.ncbi.nlm.nih.gov/pubmed/35592718
http://dx.doi.org/10.1155/2022/8239905
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author Zhang, Ning
author_facet Zhang, Ning
author_sort Zhang, Ning
collection PubMed
description Chinese classical dance is cut into the inner verve from a grasp of external form in dance instruction, and the aesthetic fashion and artistic norms of classical dance are established with historical depth. The “professional specificity” of characters and the “language description” of plots are eliminated in Chinese classical dance creation, highlighting the contemporary spirit of classical dance creation. Chinese classical dance was born during the early years of the People's Republic of China. The term “classical dance” did not refer to all Chinese classical dances at the time; rather, it referred to a dance form that embodied China's national spirit and had a classical cultural heritage based on Chinese traditional dance. The average frequency of step-over was 0.9, which was higher than the average rate of basic turnover of 0.75 and step-by-step turnover of 0.5, according to the results of the SPSS19.0 analysis. As a result, except for a few points with loud noise, it can be concluded that stepping over is an effective feature. The recognition model of the somersault posture of classical dance is studied in this paper, a database for real-time acquisition of three-dimensional data of human motion is established, and the Google model of human body characteristics is obtained based on feature plane matching of human body posture, all using motion capture technology and few-shot learning. The above data has good reference and application value for improving teachers' teaching level and arousing students' learning enthusiasm in the dance teaching process when applied to posture teaching and analysis. The captured data can convert human motion in real three-dimensional space into data in virtual three-dimensional space. Motion capture technology converts human motion information into a technology that can be recognized by computers.
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spelling pubmed-91138802022-05-18 Identification Model of Writhing Posture of Classical Dance Based on Motion Capture Technology and Few-Shot Learning Zhang, Ning Comput Intell Neurosci Research Article Chinese classical dance is cut into the inner verve from a grasp of external form in dance instruction, and the aesthetic fashion and artistic norms of classical dance are established with historical depth. The “professional specificity” of characters and the “language description” of plots are eliminated in Chinese classical dance creation, highlighting the contemporary spirit of classical dance creation. Chinese classical dance was born during the early years of the People's Republic of China. The term “classical dance” did not refer to all Chinese classical dances at the time; rather, it referred to a dance form that embodied China's national spirit and had a classical cultural heritage based on Chinese traditional dance. The average frequency of step-over was 0.9, which was higher than the average rate of basic turnover of 0.75 and step-by-step turnover of 0.5, according to the results of the SPSS19.0 analysis. As a result, except for a few points with loud noise, it can be concluded that stepping over is an effective feature. The recognition model of the somersault posture of classical dance is studied in this paper, a database for real-time acquisition of three-dimensional data of human motion is established, and the Google model of human body characteristics is obtained based on feature plane matching of human body posture, all using motion capture technology and few-shot learning. The above data has good reference and application value for improving teachers' teaching level and arousing students' learning enthusiasm in the dance teaching process when applied to posture teaching and analysis. The captured data can convert human motion in real three-dimensional space into data in virtual three-dimensional space. Motion capture technology converts human motion information into a technology that can be recognized by computers. Hindawi 2022-05-10 /pmc/articles/PMC9113880/ /pubmed/35592718 http://dx.doi.org/10.1155/2022/8239905 Text en Copyright © 2022 Ning Zhang. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Ning
Identification Model of Writhing Posture of Classical Dance Based on Motion Capture Technology and Few-Shot Learning
title Identification Model of Writhing Posture of Classical Dance Based on Motion Capture Technology and Few-Shot Learning
title_full Identification Model of Writhing Posture of Classical Dance Based on Motion Capture Technology and Few-Shot Learning
title_fullStr Identification Model of Writhing Posture of Classical Dance Based on Motion Capture Technology and Few-Shot Learning
title_full_unstemmed Identification Model of Writhing Posture of Classical Dance Based on Motion Capture Technology and Few-Shot Learning
title_short Identification Model of Writhing Posture of Classical Dance Based on Motion Capture Technology and Few-Shot Learning
title_sort identification model of writhing posture of classical dance based on motion capture technology and few-shot learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9113880/
https://www.ncbi.nlm.nih.gov/pubmed/35592718
http://dx.doi.org/10.1155/2022/8239905
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