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Research on simulation of 3D human animation vision technology based on an enhanced machine learning algorithm

  • S.I.: Artificial Intelligence Technologies in Sports and Art Data Applications
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A Correction to this article was published on 28 April 2022

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Abstract

This paper provides an in-depth analysis and study of the simulation of 3D human animation visualization techniques by enhancing machine learning algorithms. Based on the statistical analysis of the data obtained from different measurement methods, the extraction of human body feature parameters based on millimeter-wave point cloud data is realized, and the 3D reconstruction and simulation of the human body are realized using parametric human modeling software. In video-based action recognition, most methods are data-driven and use deep networks to automatically learn features of the entire video image. In this process, specific research on human actions is not included or reflected. However, human action recognition is a processing of the semantic level of video content. Realizing universal human action recognition requires a semantic understanding of human behavior. Firstly, the geometric feature analysis of the 3D scanned human model is performed to extract the human body shape characteristic parameters, and the research on the analysis and estimation methods of body shape characteristic parameters is carried out to establish the human body shape parameter relationship model; then, the millimeter-wave point cloud is calculated and measured, the Li group features extracted using the group skeletal representation model with high data dimensionality, to be able to process the high-dimensional data, while reducing the complexity of the recognition process and speeding up the computation, feature learning and classification are performed with convolutional neural networks. To verify the better library portability and robustness of the method in this paper, the method was tested on a self-built human action database in the laboratory, and an average recognition rate of 97.26% was achieved. Meanwhile, this paper investigates the natural interaction application of virtual characters in a virtual learning environment based on human action recognition. Four testers tested the virtual human–computer interaction system of this paper, respectively, and the final test results show that the system has flexibility and stability.

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References

  1. Alexopoulos K, Nikolakis N, Chryssolouris G (2020) Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing. Int J Comput Integr Manuf 33(5):429–439

    Article  Google Scholar 

  2. Yang J, Wang C, Jiang B et al (2020) Visual perception enabled industry intelligence: state of the art, challenges and prospects. IEEE Trans Ind Inf 17(3):2204–2219

    Article  Google Scholar 

  3. Ghana S, Singh S, Jalali A et al (2020) Adaptive visual learning using augmented reality and machine learning techniques. J Comput Theor Nanosci 17(11):4952–4956

    Article  Google Scholar 

  4. Tian Y, Li Y, Pan L et al (2020) Research on group animation design technology based on artificial fish swarm algorithm. J Intell Fuzzy Syst 38(2):1137–1145

    Article  Google Scholar 

  5. Myszczynska MA, Ojamies PN, Lacoste AMB et al (2020) Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat Rev Neurol 16(8):440–456

    Article  Google Scholar 

  6. Liu MY, Huang X, Yu J et al (2021) Generative adversarial networks for image and video synthesis: algorithms and applications. Proc IEEE 109(5):839–862

    Article  Google Scholar 

  7. Smith B, Wu C, Wen H et al (2020) Constraining dense hand surface tracking with elasticity. ACM Trans Graph (TOG) 39(6):1–14

    Article  Google Scholar 

  8. Puzyrev D, Harth K, Trittel T et al (2020) Machine learning for 3D particle tracking in granular gases. Microgravity Sci Technol 32(5):897–906

    Article  Google Scholar 

  9. Ukrit MF, Nithyakani P (2020) The systematic review on gait analysis: trends and developments. Eur J Mol Clin Med 7(6):1636–1654

    Google Scholar 

  10. Siddique S, Chow JCL (2021) Machine learning in healthcare communication. Encyclopedia 1(1):220–239

    Article  Google Scholar 

  11. Mourdi Y, Sadgal M, Fathi WB et al (2020) A machine learning based approach to enhance MOOC users’ classification. Turk Online J Distance Educ 21(2):47–68

    Article  Google Scholar 

  12. Khan SA, Khan MA, Song OY et al (2020) Medical imaging fusion techniques: a survey benchmark analysis, open challenges and recommendations. J Med Imaging Health Inf 10(11):2523–2531

    Article  Google Scholar 

  13. Carrozzino MA, Galdieri R, Machidon OM et al (2020) Do virtual humans dream of digital sheep? IEEE Comput Graphics Appl 40(4):71–83

    Article  Google Scholar 

  14. de Belen RAJ, Bednarz T, Sowmya A et al (2020) Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019. Transl Psychiatry 10(1):1–20

    Article  Google Scholar 

  15. Fu K, Peng J, He Q et al (2021) Single image 3D object reconstruction based on deep learning: a review. Multimed Tools Appl 80(1):463–498

    Article  Google Scholar 

  16. Shimada S, Golyanik V, Xu W et al (2020) Physcap: physically plausible monocular 3d motion capture in real time. ACM Trans Graph (TOG) 39(6):1–16

    Article  Google Scholar 

  17. Sagayam KM, Timothy AJ, Ho CC et al (2020) Augmented reality-based solar system for e-magazine with 3-D audio effect. Int J Simul Process Model 15(6):524–534

    Article  Google Scholar 

  18. Wang ZJ, Turko R, Shaikh O et al (2020) CNN explainer: learning convolutional neural networks with interactive visualization. IEEE Trans Visual Comput Graph 27(2):1396–1406

    Article  Google Scholar 

  19. Mahayuddin ZR, Saif AFMS (2020) A comprehensive review towards segmentation and detection of cancer cell and tumor for dynamic 3D reconstruction. Asia-Pacific J Inf Technol Multimed 9(1):28–39

    Article  Google Scholar 

  20. Devi PRS, Baskaran R (2021) SL2E-AFRE: personalized 3D face reconstruction using autoencoder with simultaneous subspace learning and landmark estimation. Appl Intell 51(4):2253–2268

    Article  Google Scholar 

  21. Zhang S, Callaghan V (2021) Real-time human posture recognition using an adaptive hybrid classifier. Int J Mach Learn Cybern 12(2):489–499

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by High-level Science Foundation of Qingdao Agricultural University (Grant number 6631121712).

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Authors and Affiliations

  1. Academic Affairs Office, Qingdao Agricultural University, Qingdao, 266109, China

    Henning Yuan

  2. Department of Formative Convergence Arts, Huxi University, Seoul, 31499, Korea

    Henning Yuan & Jong Han Lee

  3. Animation and Communication College, Qingdao Agricultural University, Qingdao, 266109, China

    Sai Zhang

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  1. Henning Yuan
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  2. Jong Han Lee
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  3. Sai Zhang
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Correspondence to Sai Zhang.

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Yuan, H., Lee, J.H. & Zhang, S. Research on simulation of 3D human animation vision technology based on an enhanced machine learning algorithm. Neural Comput & Applic 35, 4243–4254 (2023). https://doi.org/10.1007/s00521-022-07083-x

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  • DOI: https://doi.org/10.1007/s00521-022-07083-x

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