  Learning-based approaches to monocular motion capture have recently shown
promising results by learning to regress in a data-driven manner. However, due
to the challenges in data collection and network designs, it remains
challenging for existing solutions to achieve real-time full-body capture while
being accurate in world space. In this work, we introduce ProxyCap, a
human-centric proxy-to-motion learning scheme to learn world-space motions from
a proxy dataset of 2D skeleton sequences and 3D rotational motions. Such proxy
data enables us to build a learning-based network with accurate world-space
supervision while also mitigating the generalization issues. For more accurate
and physically plausible predictions in world space, our network is designed to
learn human motions from a human-centric perspective, which enables the
understanding of the same motion captured with different camera trajectories.
Moreover, a contact-aware neural motion descent module is proposed in our
network so that it can be aware of foot-ground contact and motion misalignment
with the proxy observations. With the proposed learning-based solution, we
demonstrate the first real-time monocular full-body capture system with
plausible foot-ground contact in world space even using hand-held moving
cameras. Our project page is https://zhangyux15.github.io/ProxyCapV2.
