Optimization of Retargeting Motion Capture for Remo Dance Using Fuzzy Logic

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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.