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.

为帮助智能可穿戴设备研究人员选择最佳的动作捕捉（MoCap）的真实方法，我们提出了一个特定的基於衣物的基准测试，DrapeMoCapBench（DMCB），专门用于评估基于光学标记和无标记的 MoCap 的性能。DMCB 利用大量的真实世界记录的 MoCap 数据集并进行平行的三维物理模拟，包括六级的紧身及松弛程度、三个级别的动作和六种体型 - 性别组合，以评估基于光学标记和低成本的无标记 MoCap 方法在不同场景下的性能，并得出结论无标记 MoCap 在日常活动中稍微优于标记 MoCap，成为可穿戴研究中一种可取且具有成本效益的选择。