Deep Learning-Based Motion Reconstruction Using Tracker Sensors
Author:
Funder
Ministry of Science ICT and Future Planning
Hanyang University
Publisher
Korea Computer Graphics Society
Subject
General Medicine
Link
http://journal.cg-korea.org/download/download_pdf?doi=10.15701/kcgs.2023.29.5.11
Reference22 articles.
1. T. Sweeney, “Foundational principles & technologies for the metaverse,” in ACM SIGGRAPH 2019 Talks, SIGGRAPH ’19, (New York, NY, USA), Association for Computing Machinery, 2019. 10.1145/3306307.3339844
2. A. Chatzitofis, G. Albanis, N. Zioulis, and S. Thermos, “A low-cost realtime motion capture system,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21421–21426, 2022. 10.1109/CVPR52688.2022.02078
3. J. Kim, D. Kang, Y. Lee, and T. Kwon, “Real-time interactive animation system for low-priced motion capture sensors,” Journal of the Korea Computer Graphics Society, vol. 28, no. 2, pp. 29–41, 2022. 10.15701/kcgs.2022.28.2.29
4. B. Van Hooren, N. Pecasse, K. Meijer, and J. M. N. Essers, “The accuracy of markerless motion capture combined with computer vision techniques for measuring running kinematics,” Scandinavian Journal of Medicine & Science in Sports, vol. 33, no. 6, pp. 966–978, 2023. 10.1111/sms.14319 36680411
5. S. L. Colyer, M. Evans, D. P. Cosker, and A. I. T. Salo, “A review of the evolution of vision-based motion analysis and the integration of advanced computer vision methods towards developing a markerless system,” Sports Medicine - Open, 2018/06/05. 10.1186/s40798-018-0139-y 29869300 PMC5986692
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