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Gap filling algorithm for motion capture data to create realistic vehicle animation

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Języki publikacji
EN
Abstrakty
EN
The dynamic development of the entertainment market entails the need to develop new methods enabling the application of current scientific achievements. Motion capture is one of the cutting-edge technologies that plays a key role in movement and trajectory computer mapping. The use of optical systems allows one to obtain highly precise motion data that is often applied in computer animations. This study aimed to define the research methodology proposed to analyze the movement of remotely controlled cars utilizing developed gap filling algorithm, a part of post-processing, for creating realistic vehicle animation. On a specially prepared model, six various types of movements were recorded, such as: driving straight line forward, driving straight line backwards, driving on a curve to the left, driving on a curve to the right and driving around a roundabout on both sides. These movements were recorded using a VICON passive motion capture system. As a result, three-dimensional models of vehicles were created that were further post-processed, mainly by filling in the gaps in the trajectories. The case study highlighted problems such as missing points at the beginning and end of the recordings. Therefore, algorithm was developed to solve the above-mentioned problem and allowed for obtaining an accurate movement trajectory throughout the entire route. Realistic animations were created from the prepared data. The preliminary studies allowed one for the verification of the research method and implemented algorithm for obtaining animations reflecting accurate movements.
Słowa kluczowe
Rocznik
Strony
17--33
Opis fizyczny
Bibliogr. 34 poz., fig., tab.
Twórcy
  • Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Department of Computer Science, Poland
  • Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Department of Computer Science, Poland
Bibliografia
  • [1] Ardestani, M. M. M., & Yan, H. (2022). Noise reduction in human motion-captured signals for computer animation based on B-Spline filtering. Sensors, 22(12), 4629. https://doi.org/10.3390/s22124629
  • [2] Asraf, S. M. H., Abdullasim, N., & Romli, R. (2020). Hybrid animation: implementation of motion capture. IOP Conference Series. Materials Science and Engineering, 767, 012065. https://doi.org/10.1088/1757-899x/767/1/012065
  • [3] Autodesk. (n.d. a). 3DS Max Software. https://www.autodesk.com/products/3ds-max
  • [4] Autodesk. (n.d. b). Maya Software. https://www.autodesk.com/products/maya
  • [5] Cao, Y., Zhao, Y., Hu, Y., & Lin, B. (2020). Research on physically-based computer animation. 2020 2nd International Conference on Information Technology and Computer Application (ITCA) (pp. 186-190). IEEE. https://doi.org/10.1109/itca52113.2020.00046
  • [6] Chung, Y., Annaswamy, T. M., & Prabhakaran, B. (2022). Design of calibration module for a home-based immersive game using camera motion capture system. 2022 ACM Symposium on Spatial User Interaction. Association for Computing Machinery. https://doi.org/10.1145/3565970.3567694
  • [7] Guo, Y., & Zhong, C. (2022). Motion capture technology and its applications in film and television animation. Advances in Multimedia, 2022(1), 6392168. https://doi.org/10.1155/2022/6392168
  • [8] Intel RealSense. (2024, May 17). Intel® RealSenseTM Computer Vision - Depth and Tracking cameras. Intel® RealSenseTM Depth and Tracking Cameras. https://www.intelrealsense.com/
  • [9] Lam, W. W. T., Tang, Y. M., & Fong, K. N. K. (2023). A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation. Journal of Neuroengineering and Rehabilitation, 20, 57. https://doi.org/10.1186/s12984-023-01186-9
  • [10] Lei, Q. (2019). Research on animation and its motion capture technology. 2018 International Conference on Data Processing, 2018 International Conference on Data Processing, Artificial Intelligence, and Communications (DPAIC 2018) (pp. 121-124). Francis Academic Press.
  • [11] Liu, S., Zhang, J., Zhang, Y., & Zhu, R. (2020). A wearable motion capture device able to detect dynamic motion of human limbs. Nature Communications, 11, 5615. https://doi.org/10.1038/s41467-020-19424-2
  • [12] Lopez, S., Johnson, C., Frankston, N., Ruh, E., McClincy, M., & Anderst, W. (2024). Accuracy of conventional motion capture in measuring hip joint center location and hip rotations during gait, squat, and step-up activities. Journal of Biomechanics, 167, 112079. https://doi.org/10.1016/j.jbiomech.2024.112079
  • [13] Lugrís, U., Pérez-Soto, M., Michaud, F., & Cuadrado, J. (2023). Human motion capture, reconstruction, and musculoskeletal analysis in real time. Multibody System Dynamics, 60, 3-25. https://doi.org/10.1007/s11044-023-09938-0
  • [14] Mousas, C., & Anagnostopoulos, C.-N. (2017). Real-time performance-driven finger motion synthesis. Computers & Graphics, 65, 1–11. https://doi.org/10.1016/j.cag.2017.03.001
  • [15] Naik, M., Suryawanshi, Y., & Atre, A. (2023). Unleashing the power of animation in marketing: Insights and implications. The Online Journal of Distance Education and e-Learning, 11(02), 2621-2630.
  • [16] Powroznik, P., Skublewska-Paszkowska, M., Karczmarek, P., & Lukasik, E. (2022). Aggregation of tennis groundstrokes on the basis of the choquet integral and its generalizations. 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE. https://doi.org/10.1109/fuzz-ieee55066.2022.9882592
  • [17] Praveen, C. K., & Srinivasan, K. (2022). Psychological impact and influence of animation on viewer’s visual attention and cognition: A systematic literature review, open challenges, and future research directions. Computational and Mathematical Methods in Medicine, 2022(1), 8802542. https://doi.org/10.1155/2022/8802542
  • [18] Rupnawar, N. R., Swami, N. D., Kshirsagar, N. A., Sayyed, N. A., Samleti, N. R., & Chandane, N. P. E. R. (2024). A real-time motion capture system for 3-D virtual characters. International Journal of Advanced Research in Science, Communication and Technology, 4(2), 108–115. https://doi.org/10.48175/ijarsct-18814
  • [19] Salonen, S. (2021). Motion Capture in 3D animation. Tampere University.
  • [20] Sharma, S., Verma, S., Kumar, M., & Sharma, L. (2019). Use of motion capture in 3D animation: Motion capture systems, challenges, and recent trends. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon) (pp. 289-294). IEEE. https://doi.org/10.1109/comitcon.2019.8862448
  • [21] Skublewska-Paszkowska, M., & Powroznik, P. (2023). Temporal pattern attention for multivariate time series of tennis strokes classification. Sensors, 23(5), 2422. https://doi.org/10.3390/s23052422
  • [22] Skublewska-Paszkowska, M., Łukasik, E., & Smołka, J. (2012). Analysis on motion interpolation methods. Actual Problems of Economics, 11(137), 448–455.
  • [23] Skublewska-Paszkowska, M., Lukasik, E., Szydlowski, B., Smolka, J., & Powroznik, P. (2020). Recognition of tennis shots using convolutional neural networks based on three-dimensional data. In A. Gruca, T. Czachórski, S. Deorowicz, K. Harężlak, & A. Piotrowska (Eds.), Man-Machine Interactions 6 (Vol. 1061, pp. 146–155). Springer International Publishing. https://doi.org/10.1007/978-3-030-31964-9_14
  • [24] Skublewska-Paszkowska, M., Powroznik, P., & Lukasik, E. (2022). Attention temporal graph convolutional network for tennis groundstrokes phases classification. 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE. https://doi.org/10.1109/fuzz-ieee55066.2022.9882822
  • [25] Skublewska-Paszkowska, M., Powroźnik, P., Barszcz, M., & Dziedzic, K. (2023). Dual attention graph convolutional neural network to support mocap data animation. Advances in Science and Technology Research Journal, 17(5), 313-325. https://doi.org/10.12913/22998624/171592
  • [26] Smirnova, V., Khamatnurova, R., Kharin, N., Yaikova, E., Baltina, T., & Sachenkov, O. (2022). The automatization of the GAIT analysis by the Vicon video system: A pilot study. Sensors, 22(19), 7178. https://doi.org/10.3390/s22197178
  • [27] Smołka, J., & Skublewska-Paszkowska, M. (2014). Comparison of interpolation methods based on real human motion data. Przegląd Elektrotechniczny, 90(10), 226-229. https://doi.org/10.12915/pe.2014.10.54
  • [28] Topley, M., & Richards, J. G. (2020). A comparison of currently available optoelectronic motion capture systems. Journal of Biomechanics, 106, 109820. https://doi.org/10.1016/j.jbiomech.2020.109820
  • [29] Vicon. (2024, June 25). https://www.vicon.com/
  • [30] Vicon. (2024, May 24). Nexus Version 2.16. https://www.vicon.com/software/nexus/
  • [31] Vicon. (2024, May 24b). Tracker: software. Version 3.1. https://vicon.com/software/tracker/
  • [32] Wibowo, M. C., Nugroho, S., & Wibowo, A. (2024). The use of motion capture technology in 3D animation. International Journal of Computing and Digital Systems, 15(01). http://dx.doi.org/10.12785/ijcds//150169
  • [33] Yun, G., Lee, H., Han, S., & Choi, S. (2021). Improving viewing experiences of first-person shooter gameplays with automatically-generated motion effects. 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21) (pp. 1-14). Association for Computing Machinery. https://doi.org/10.1145/3411764.3445358
  • [34] Zhu, Y. (2019). Application of motion capture technology in 3D animation creation. 3rd International Conference on Culture, Education and Economic Development of Modern Society (ICCESE 2019) (pp. 452-456). Atlantis Press. https://doi.org/10.2991/iccese-19.2019.101
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8951809f-74af-4ae1-8778-c9976f5f6632
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