Abstract
In support of smart wearable researchers striving to select optimal ground truth methods for motion capture across a spectrum of loose garment types, we present an extended benchmark named DrapeMoCapBench (DMCB+). This augmented benchmark incorporates a more intricate limb-wise Motion Capture (MoCap) accuracy analysis, and enhanced drape calculation, and introduces a novel benchmarking tool that encompasses multicamera deep learning MoCap methods. DMCB+ is specifically designed to evaluate the performance of both optical marker-based and markerless MoCap techniques, taking into account the challenges posed by various loose garment types. While high-cost marker-based systems are acknowledged for their precision, they often require skin-tight markers on bony areas, which can be impractical with loose garments. On the other hand, markerless MoCap methods driven by computer vision models have evolved to be more cost-effective, utilizing smartphone cameras and exhibiting promising results. Utilizing real-world MoCap datasets, DMCB+ conducts 3D physics simulations with a comprehensive set of variables, including six drape levels, three motion intensities, and six body-gender combinations. The extended benchmark provides a nuanced analysis of advanced marker-based and markerless MoCap techniques, highlighting their strengths and weaknesses across distinct scenarios. In particular, DMCB+ reveals that when evaluating casual loose garments, both marker-based and markerless methods exhibit notable performance degradation (>10 cm). However, in scenarios involving everyday activities with basic and swift motions, markerless MoCap outperforms marker-based alternatives. This positions markerless MoCap as an advantageous and economical choice for wearable studies. The inclusion of a multicamera deep learning MoCap method in the benchmarking tool further expands the scope, allowing researchers to assess the capabilities of cutting-edge technologies in diverse motion capture scenarios.
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