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Optimization of Retargeting Motion Capture for Remo Dance Using Fuzzy Logic
Corresponding Author(s) : Didit Prasetyo
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 3, August 2026 (Article in Progress)
Abstract
Retargeting motion capture for traditional dance animation faces challenges in maintaining biomechanical accuracy while preserving cultural expressiveness, especially when human motion data are transferred to character models with different skeletal structures. This research aims to optimize the retargeting of East Java Remo Dance through an adaptive artificial intelligence-based evaluation approach. The Remo dance movement was recorded using a multi-camera optical motion capture system and retargeted to two types of 3D characters: realistic and stylized. The evaluation was conducted using quantitative metrics (Mean Squared Error, Structural Similarity Index, Dynamic Time Warping, and Kalman Filtering) as well as a qualitative approach through Laban Movement Analysis. Subsequently, Mamdani fuzzy logic was integrated to synthesize all these parameters into the Fuzzy Retargeting Quality Score (FRQS). The results showed that the realistic character had higher movement accuracy (MSE = 0.0032; SSI = 0.89; DTW = 0.92) and obtained an FRQS value of 86.4 (very optimal category), whereas the stylized character obtained an FRQS of 71.2 (moderately optimal), reflecting a compromise between movement precision and visual appeal. The integration of fuzzy logic allows for more contextual and human-centric retargeting evaluation, as well as strengthening the dual-model approach to the preservation and education of traditional dance based on digital animation.
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- H. Sun, “Optimization of AI-Generated Animation Based on Computer-Aided Design in the Digital Media Environment,” Journal of Cases on Information Technology, vol. 27, no. 1, pp. 1–12, Jun. 2025, http://doi.org/10.4018/JCIT.382562
References
S. Basri and E. Sari, “Tari Remo (NGREMONG): Sebuah Analisis Teori Semiotika Roland Barthes tentang Makna Denotasi dan Konotasi dalam Tari Remo (NGREMONG),” GETER : Jurnal Seni Drama, Tari dan Musik, vol. 2, no. 1, pp. 55–69, Apr. 2019, http://doi.org/10.26740/geter.v2n1.p55-69
M. Zhu, “Dance Basic Training Methods Based on Motion Capture Technology,” in 2021 2nd International Conference on Computers, Information Processing and Advanced Education, New York, NY, USA: ACM, May 2021, pp. 1278–1281. http://doi.org/10.1145/3456887.3457507
W.-S. Kim, E.-J. Son, J.-H. Sung, S.-W. Lee, and S.-M. Choi, “Creating Traditional Dance Animation Using Low-Cost Motion Capture Equipment,” Journal of Korean Dance, vol. 64, pp. 49–63, Feb. 2024, http://doi.org/10.52892/RIKD.2024.64.3
Y. Peng, “Research on Dance Teaching Based on Motion Capture System,” Math Probl Eng, vol. 2022, pp. 1–8, May 2022, http://doi.org/10.1155/2022/1455849
M. F. Mohd Herrow and N. Azraai, “Digital Micro Visualization Of Movements Through Motion Capture: A Case Study Of Joget Serampang Laut,” Idealogy Journal, vol. 8, no. 2, Sep. 2023, http://doi.org/10.24191/idealogy.v8i2.473
A. Camurri et al., “WhoLoDancE: Towards a methodology for selecting Motion Capture Data across different Dance Learning Practice,” in Proceedings of the 3rd International Symposium on Movement and Computing, New York, NY, USA: ACM, Jul. 2016, pp. 1–2. http://doi.org/10.1145/2948910.2948912
C. Liu, B. Zhang, and W. Wang, “A Pose and Shape-Aware Cross-Skeleton Motion Retargeting Framework,” in 2023 IEEE International Conference on Big Data (BigData), IEEE, Dec. 2023, pp. 2534–2540. http://doi.org/10.1109/BigData59044.2023.10386650
U. Celikcan, I. O. Yaz, and T. Capin, “Example‐Based Retargeting of Human Motion to Arbitrary Mesh Models,” Computer Graphics Forum, vol. 34, no. 1, pp. 216–227, Feb. 2015, http://doi.org/10.1111/cgf.12507
E. Hegarini, Dharmayanti, and A. Syakur, “Indonesian traditional dance motion capture documentation,” in 2016 2nd International Conference on Science and Technology-Computer (ICST), IEEE, Oct. 2016, pp. 108–111. http://doi.org/10.1109/ICSTC.2016.7877357
A. Zhang and Y. Li, “Optimizing Motion Capture Data in Animation Sequences Using Machine Learning Techniques,” in 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS), IEEE, Jul. 2024, pp. 116–120. http://doi.org/10.1109/ICPICS62053.2024.10797215
Y. Zeng and S. Zhao, “Application of Real-time Motion Capture Technology in Street Dance Movement Analysis and Optimization,” Applied Mathematics and Nonlinear Sciences, vol. 9, no. 1, Jan. 2024, http://doi.org/10.2478/amns-2024-1913
A. Aristidou, E. Stavrakis, and Y. Chrysanthou, “LMA-Based Motion Retrieval for Folk Dance Cultural Heritage,” 2014, pp. 207–216. http://doi.org/10.1007/978-3-319-13695-0_20
N. Hube, M. Reinelt, K. Vidackovic, and M. Sedlmair, “A study on the influence of situations on personal avatar characteristics,” Vis Comput Ind Biomed Art, vol. 7, no. 1, p. 23, Sep. 2024, http://doi.org/10.1186/s42492-024-00174-7
T. Peng, “Quantitative assessment of human motion for health and rehabilitation: A novel fuzzy comprehensive evaluation approach,” SLAS Technol, vol. 29, no. 5, p. 100181, Oct. 2024, http://doi.org/10.1016/j.slast.2024.100181
L. Qianwen, “Application of motion capture technology based on wearable motion sensor devices in dance body motion recognition,” Measurement: Sensors, vol. 32, p. 101055, Apr. 2024, http://doi.org/10.1016/j.measen.2024.101055
N. Ramadhani, D. Prasetyo, A. M. Shiddiqi, I. R. Mutiaz, and M. Hariadi, “Logic-Ca: A Fuzzy Logic-Based Framework for Enhancing the Camera Angles and the Field of View in Traditional Dance Documentation,” Engineering, Technology & Applied Science Research, vol. 15, no. 5, pp. 26462–26470, Oct. 2025, http://doi.org/10.48084/etasr.12505
G. Deglorie, K. Samyn, and P. Lambert, “Procedural Animation of Human Interaction using Inverse Kinematics and Fuzzy Logic,” in Proceedings of the 10th International Conference on Computer Graphics Theory and Applications, SCITEPRESS - Science and and Technology Publications, 2015, pp. 340–347. http://doi.org/10.5220/0005310603400347
L. Peng, “Neuro-Fuzzy Logic for Automatic Animation Scene Generation in Movie Arts in Digital Media Technology,” International Journal of Computational Intelligence Systems, vol. 17, no. 1, p. 301, Dec. 2024, http://doi.org/10.1007/s44196-024-00709-z
A. Szulc, P. Prokopowicz, K. Buśko, and D. Mikołajewski, “Model of the Performance Based on Artificial Intelligence–Fuzzy Logic Description of Physical Activity,” Sensors, vol. 23, no. 3, p. 1117, Jan. 2023, http://doi.org/10.3390/s23031117
Y. Shi, “Research on the Inheritance Method of Minority Music and Dance Art based on Motion Capture Technology,” in Proceedings of the 1st International Symposium on Education, Culture and Social Sciences (ECSS 2019), Paris, France: Atlantis Press, 2019. http://doi.org/10.2991/ecss-19.2019.44
C. Zhu and C. Joslin, “A review of motion retargeting techniques for 3D character facial animation,” Comput Graph, vol. 123, p. 104037, Oct. 2024, http://doi.org/10.1016/j.cag.2024.104037
M. Wang and R. Yu, “Digital production and realization for traditional dance movements based on Motion Capture Technology,” The Frontiers of Society, Science and Technology, vol. 4, no. 11, 2022, http://doi.org/10.25236/FSST.2022.041102
C. Li and Y. Yang, “Research on Remote Dance Motion Capture Evaluation System and Dance Injury Prevention Based on Intelligent Terminal,” 2024, pp. 477–482. http://doi.org/10.1007/978-981-99-9538-7_74
Y. Zeng and S. Zhao, “Application of Real-time Motion Capture Technology in Street Dance Movement Analysis and Optimization,” Applied Mathematics and Nonlinear Sciences, vol. 9, no. 1, Jan. 2024, http://doi.org/10.2478/amns-2024-1913
C. Lu, “Feature data analysis of dance movements by motion capture,” Journal of Measurements in Engineering, vol. 13, no. 3, pp. 701–708, Sep. 2025, http://doi.org/10.21595/jme.2025.24742
J. N. Mindoro, E. D. Festijo, and Ma. T. G. de Guzman, “A Comparative Study of Deep Transfer Learning Techniques for Cultural (Aeta) Dance Classification utilizing Skeleton-Based Choreographic Motion Capture Data,” in 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), IEEE, Mar. 2021, pp. 74–79. http://doi.org/10.1109/ICCIKE51210.2021.9410796
R. S. Almeida, F. Vasconcelos da Silva, and S. S. V. Vianna, “Combining the bow-tie method and fuzzy logic using Mamdani inference model,” Process Safety and Environmental Protection, vol. 169, pp. 159–168, Jan. 2023, http://doi.org/10.1016/j.psep.2022.11.005
H. Wang, Y. Song, W. Jiang, and T. Wang, “A Music-Driven Dance Generation Method Based on a Spatial-Temporal Refinement Model to Optimize Abnormal Frames,” Sensors, vol. 24, no. 2, p. 588, Jan. 2024, http://doi.org/10.3390/s24020588
H. Sun, “Optimization of AI-Generated Animation Based on Computer-Aided Design in the Digital Media Environment,” Journal of Cases on Information Technology, vol. 27, no. 1, pp. 1–12, Jun. 2025, http://doi.org/10.4018/JCIT.382562