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

This paper proposes a novel application system for the generation of
three-dimensional (3D) character animation driven by markerless human body
motion capturing. The entire pipeline of the system consists of five stages: 1)
the capturing of motion data using multiple cameras, 2) detection of the
two-dimensional (2D) human body joints, 3) estimation of the 3D joints, 4)
calculation of bone transformation matrices, and 5) generation of character
animation. The main objective of this study is to generate a 3D skeleton and
animation for 3D characters using multi-view images captured by ordinary
cameras. The computational complexity of the 3D skeleton reconstruction based
on 3D vision has been reduced as needed to achieve frame-by-frame motion
capturing. The experimental results reveal that our system can effectively and
efficiently capture human actions and use them to animate 3D cartoon characters
in real-time.

本文描述了一种基于无标记人体动作捕获的三维角色动画生成系统，采用视角多样的相机捕捉人体动作信息，并通过多种技术手段计算出骨骼变换状况，从而能够高效准确地实现三维骨骼重建与实时动画生成。