  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.

Computer Vision and Pattern Recognition;Graphics