ProxyCap: Real-time Monocular Full-body Capture in World Space via Human-Centric Proxy-to-Motion Learning

Understand

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.