Motion capture in humanoid model with Unity engine using Kinect V2
DOI:
10.47709/cnahpc.v3i2.1067Keywords:
: Animation, Collada, Video Game, Motion Capture, Motion Capture Markerless, OpenNI, Unity Engine.Dimension Badge Record
Abstract
Animation is a technique to create illusion of motion, which is created by displaying a series of motionless pictures in sequence or using a spine/bones to create motion that looks real. All this time, the process of creating an animation still using traditional technique which requires special skills and take some time to finish a complicated animation which is used in movies or video games. Motion capture is an animation-making technique by tracking every part of body in order to find position and rotation of human’s joints which is generated by the image from the sensor. Motion capture has lots of method, such as marker base technique which use mark to track any motions. Motion capture markerless method that can capture or track motions without using any marks. Motion capture with markerless technique can be done by using RGB-Depth’s camera censor, which is by using Microsoft Kinect V2 with Kinect V2 SDK in order to make Kinect connected with computer, and using Unity Engine, a game engine that has already provided animation timeline and supports any 3D format which contains animation file that can be used in mostly other models which is using humanoid. In order to obtain motion data which will be changed into skeleton joint data, we will use connector OpenNI and 3D Collada model with (.dae) format because Collada is 3D format Open Source which is built using XML-based so that it can easily read and written back into 3D file as an output.
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References
Ariyati, S., & Misriati, T. (2016). Perancangan Animasi Interaktif Pembelajaran Asmaul Husna. Jurnal Teknik Komputer Amik Bsi, II(1), 116–121.
Borromeo, N. A. (2020). Hands-On Unity 2020 Game Development.
Buyuksalih, I., Bayburt, S., Buyuksalih, G., Baskaraca, A. P., Karim, H., & Rahman, A. A. (2017). 3D MODELLING and VISUALIZATION BASED on the UNITY GAME ENGINE - ADVANTAGES and CHALLENGES. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4(4W4), 161–166. https://doi.org/10.5194/isprs-annals-IV-4-W4-161-2017
C?lin, A. D., & Coroiu, A. (2018). Interchangeability of kinect and orbbec sensors for gesture recognition. Proceedings - 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing, ICCP 2018, 309–315. https://doi.org/10.1109/ICCP.2018.8516586
Capece, N., Erra, U., & Romaniello, G. (2018). A Low-Cost Full Body Tracking System in Virtual Reality Based on Microsoft Kinect. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10851 LNCS, 623–635. https://doi.org/10.1007/978-3-319-95282-6_44
Chatzitofis, A., Zarpalas, D., Kollias, S., & Daras, P. (2019). Deepmocap: Deep optical motion capture using multiple depth sensors and retro-reflectors. Sensors (Switzerland), 19(2), 1–26. https://doi.org/10.3390/s19020282
Chelyshkov, P., Volkov, S. A., & Babushkin, E. S. (2021). Analysis of world and domestic experience in the use of XML schemas in the implementation of information interaction during maintaining the information model of a capital construction object. IOP Conference Series: Materials Science and Engineering, 1030, 012067. https://doi.org/10.1088/1757-899x/1030/1/012067
Chung, S., Cho, S., & Kim, S. (2018). Interaction using Wearable Motion Capture for the Virtual Reality Game. Journal of The Korean Society for Computer Game, 31(3), 81–89. https://doi.org/10.22819/kscg.2018.31.3.010
Fatoni, A., & Dwi, D. (2016). Rancang Bangun Sistem Extreme Programming Sebagai Metodologi Pengembangan Sistem. Prosisko, 3(1), 1–4. http://e-jurnal.lppmunsera.org/index.php/PROSISKO/article/view/116
Halpern, J. (2019). Developing 2D Games with Unity. In Developing 2D Games with Unity. https://doi.org/10.1007/978-1-4842-3772-4
Hameed, A. (2016). Software Development Lifecycle for Extreme Programming. International Journal of Information Technology and Electrical Engineering ITEE, 5(1), 7–13.
Hegarini, E., Mutiara, A. B., Suhendra, A., Iqbal, M., & Wardijono, B. A. (2017). Similarity analysis of motion based on motion capture technology. 2016 International Conference on Informatics and Computing, ICIC 2016, 389–393. https://doi.org/10.1109/IAC.2016.7905750
Keselman, L., Woodfill, J. I., Grunnet-Jepsen, A., & Bhowmik, A. (2017). Intel R RealSense TM Stereoscopic Depth Cameras.
Kuan, Y. W., Ee, N. O., & Wei, L. S. (2019). Comparative study of intel R200, Kinect v2, and primesense RGB-D sensors performance outdoors. IEEE Sensors Journal, 19(19), 8741–8750. https://doi.org/10.1109/JSEN.2019.2920976
Kupiainen, H. (2018). EXTENDING UNITY GAME ENGINE THROUGH EDITOR SCRIPTING. October, 4.
LeMay, M. (2019). Agile for Everybody. O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.
Napoli, A., Glass, S., Ward, C., Tucker, C., & Obeid, I. (2017). Performance analysis of a generalized motion capture system using microsoft kinect 2.0. Biomedical Signal Processing and Control, 38, 265–280. https://doi.org/10.1016/j.bspc.2017.06.006
Özdem, A. (2016). Motion Capture. ??????? ????? ??????????? ??????????, XXIII(2), 1–9.
Patrizi, A., Pennestrì, E., & Valentini, P. P. (2016). Comparison between low-cost marker-less and high-end marker-based motion capture systems for the computer-aided assessment of working ergonomics. Ergonomics, 59(1), 155–162. https://doi.org/10.1080/00140139.2015.1057238
Procházka, A., Schätz, M., Vyšata, O., & Vališ, M. (2016). Microsoft Kinect visual and depth sensors for breathing and heart rate analysis. Sensors (Switzerland), 16(7), 1–11. https://doi.org/10.3390/s16070996
Ritchie, P. (2016). Practical Microsoft Visual Studio 2015. Practical Microsoft Visual Studio 2015, 1–25. https://doi.org/10.1007/978-1-4842-2313-0
Saini, N., Price, E., Tallamraju, R., Enficiaud, R., Ludwig, R., Martinovic, I., Ahmad, A., & Black, M. J. (2019). Markerless Outdoor Human Motion Capture Using Multiple Autonomous Micro Aerial Vehicles.
Taher, R. (2019). Hands-On Object-Oriented Programming with C #. Packt Publishing Ltd.
Wasenmüller, O., & Stricker, D. (2017). Comparison of kinect v1 and v2 depth images in terms of accuracy and precision. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10117 LNCS, 34–45. https://doi.org/10.1007/978-3-319-54427-4_3
Win, S., & Thein, T. L. L. (2020, February 1). Real-Time Human Motion Detection, Tracking and Activity Recognition with Skeletal Model. 2020 IEEE Conference on Computer Applications, ICCA 2020. https://doi.org/10.1109/ICCA49400.2020.9022822
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