We present a lightweight and affordable motion capture method based on two
smartwatches and a head-mounted camera. In contrast to the existing approaches
that use six or more expert-level IMU devices, our approach is much more
cost-effective and convenient. Our method can make wearable motion capture
accessible to everyone everywhere, enabling 3D full-body motion capture in
diverse environments. As a key idea to overcome the extreme sparsity and
ambiguities of sensor inputs, we integrate 6D head poses obtained from the
head-mounted cameras for motion estimation. To enable capture in expansive
indoor and outdoor scenes, we propose an algorithm to track and update floor
level changes to define head poses, coupled with a multi-stage
Transformer-based regression module. We also introduce novel strategies
leveraging visual cues of egocentric images to further enhance the motion
capture quality while reducing ambiguities. We demonstrate the performance of
our method on various challenging scenarios, including complex outdoor
environments and everyday motions including object interactions and social
interactions among multiple individuals.

通过两个智能手表和一个头戴相机，我们提出了一种轻量级且经济实惠的动作捕捉方法，与使用六个或更多专业级 IMU 设备的现有方法相比，我们的方法更具成本效益和便利性，能够使可穿戴式动作捕捉技术普及化，实现在不同环境中的 3D 全身动作捕捉。同时，我们还引入了从头戴相机获取的 6D 头部姿势，以克服传感器输入的稀疏性和模糊性，并提出了一种跟踪和更新楼层变化来定义头部姿势的算法，以及一个多阶段的基于 Transformer 的回归模块。此外，我们还引入了利用自我中心图像的视觉线索来进一步提高动作捕捉质量并减少模糊性的新策略。我们在包括复杂的室外环境和多个个体之间的物体交互和社交互动在内的各种具有挑战性的场景中展示了我们方法的性能。

智能手表和头戴式摄像头的轻量级动作捕捉

Mocap Everyone Everywhere: Lightweight Motion Capture With Smartwatches  and a Head-Mounted Camera

Some defenders of so-called `artificial intelligence' believe that machines
can understand language. In particular, S{\o}gaard has argued in this journal
for a thesis of this sort, on the basis of the idea (1) that where there is
semantics there is also understanding and (2) that machines are not only
capable of what he calls `inferential semantics', but even that they can (with
the help of inputs from sensors) `learn' referential semantics
\parencite{sogaard:2022}. We show that he goes wrong because he pays
insufficient attention to the difference between language as used by humans and
the sequences of inert of symbols which arise when language is stored on hard
drives or in books in libraries.

一些所谓 `人工智能 ' 的辩护者认为机器可以理解语言。特别是，Søgaard 在这篇论文中提出了这样的论点，即（1）哪里有语义，就会有理解，（2）机器不仅能够进行所谓的` 推理语义 '，甚至可以（通过传感器的输入帮助）` 学习 ' 指称语义。然而，我们证明他的观点是错误的，因为他未充分重视语言在人类和硬盘或书籍中存储时出现的差异。