GAP FILLING ALGORITHM FOR MOTION CAPTURE DATA TO CREATE REALISTIC VEHICLE ANIMATION
Weronika WACH
w.wach@pollub.plLublin University of Technology (Poland)
https://orcid.org/0009-0004-6164-5862
Kinga CHWALEBA
Lublin University of Technology (Poland)
https://orcid.org/0009-0007-3458-5464
Abstract
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.
Keywords:
methodology, motion capture, movement data, vehicles, animationReferences
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
Google Scholar
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
Google Scholar
Autodesk. (n.d. a). 3DS Max Software. https://www.autodesk.com/products/3ds-max
Google Scholar
Autodesk. (n.d. b). Maya Software. https://www.autodesk.com/products/maya
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
Intel RealSense. (2024, May 17). Intel® RealSenseTM Computer Vision - Depth and Tracking cameras. Intel® RealSenseTM Depth and Tracking Cameras. https://www.intelrealsense.com/
Google Scholar
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
Google Scholar
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.
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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.
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
Salonen, S. (2021). Motion Capture in 3D animation. Tampere University.
Google Scholar
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
Google Scholar
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
Google Scholar
Skublewska-Paszkowska, M., Łukasik, E., & Smołka, J. (2012). Analysis on motion interpolation methods. Actual Problems of Economics, 11(137), 448–455.
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
Vicon. (2024, June 25). https://www.vicon.com/
Google Scholar
Vicon. (2024, May 24). Nexus Version 2.16. https://www.vicon.com/software/nexus/
Google Scholar
Vicon. (2024, May 24b). Tracker: software. Version 3.1. https://vicon.com/software/tracker/
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
Authors
Weronika WACHw.wach@pollub.pl
Lublin University of Technology Poland
https://orcid.org/0009-0004-6164-5862
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