To help smart wearable researchers choose the optimal ground truth methods for motion capturing (MoCap) for all types of loose garments, we present a benchmark, DrapeMoCapBench (DMCB), specifically designed to evaluate the performance of optical marker-based and marker-less MoCap. High-cost marker-based MoCap systems are well-known as precise golden standards. However, a less well-known caveat is that they require skin-tight fitting markers on bony areas to ensure the specified precision, making them questionable for loose garments. On the other hand, marker-less MoCap methods powered by computer vision models have matured over the years, which have meager costs as smartphone cameras would suffice. To this end, DMCB uses large real-world recorded MoCap datasets to perform parallel 3D physics simulations with a wide range of diversities: six levels of drape from skin-tight to extremely draped garments, three levels of motions and six body type - gender combinations to benchmark state-of-the-art optical marker-based and marker-less MoCap methods to identify the best-performing method in different scenarios. In assessing the performance of marker-based and low-cost marker-less MoCap for casual loose garments both approaches exhibit significant performance loss (>10cm), but for everyday activities involving basic and fast motions, marker-less MoCap slightly outperforms marker-based MoCap, making it a favorable and cost-effective choice for wearable studies.

翻译：为帮助智能可穿戴研究人员为各类宽松服装选择最优运动捕捉（MoCap）真值方法，我们提出了一个专门用于评估光学标记式与无标记式动捕性能的基准——DrapeMoCapBench（DMCB）。高成本标记式动捕系统被公认为精确的黄金标准，然而鲜为人知的是，其要求标记点紧贴骨性区域才能确保指定精度，这使得其在宽松服装场景下存在局限。相比之下，基于计算机视觉模型的无标记式动捕技术历经多年发展已趋成熟，仅需智能手机摄像头即可实现极低成本。为此，DMCB利用大规模真实世界记录的运动捕捉数据集进行并行三维物理仿真，涵盖广泛多样性：从紧身到极度垂坠服装的六级垂坠程度、三种运动等级及六种体型-性别组合，以此评估当前最先进的光学标记式与无标记式动捕方法，识别不同场景下的最优方案。在评估日常宽松服装的标记式与低成本无标记式动捕性能时，两种方法均表现出显著性能下降（>10厘米），但对于包含基本动作与快速运动的日常活动而言，无标记式动捕性能略优于标记式动捕，使其成为可穿戴研究中更具成本效益的优选方案。