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。

拉拉・夏克提・斯瓦鲁普・雷，周波，苏成镐，保罗・卢科维奇

Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh, Paul Lukowicz

Enormous hand images with reliable annotations are collected through
marker-based MoCap. Unfortunately, degradations caused by markers limit their
application in hand appearance reconstruction. A clear appearance recovery
insight is an image-to-image translation trained with unpaired data. However,
most frameworks fail because there exists structure inconsistency from a
degraded hand to a bare one. The core of our approach is to first disentangle
the bare hand structure from those degraded images and then wrap the appearance
to this structure with a dual adversarial discrimination (DAD) scheme. Both
modules take full advantage of the semi-supervised learning paradigm: The
structure disentanglement benefits from the modeling ability of ViT, and the
translator is enhanced by the dual discrimination on both translation processes
and translation results. Comprehensive evaluations have been conducted to prove
that our framework can robustly recover photo-realistic hand appearance from
diverse marker-contained and even object-occluded datasets. It provides a novel
avenue to acquire bare hand appearance data for other downstream learning
problems.The codes will be publicly available at this https URL

该研究提出了一种基于半监督学习的框架，使用图像转换技术从含有标记的手部动作捕捉数据集（MoCap）中恢复真实纹理，实现了裸手结构和外观的分离，成功地生成了逼真的手部外观图像。