Computer Science > Computer Vision and Pattern Recognition
[Submitted on 3 Jul 2023 (v1), last revised 25 Dec 2023 (this version, v3)]
Title:ProxyCap: Real-time Monocular Full-body Capture in World Space via Human-Centric Proxy-to-Motion Learning
View PDF HTML (experimental)Abstract: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 this https URL.
Submission history
From: Liangxiao Hu [view email][v1] Mon, 3 Jul 2023 17:59:45 UTC (17,530 KB)
[v2] Wed, 13 Dec 2023 15:26:02 UTC (5,421 KB)
[v3] Mon, 25 Dec 2023 12:20:36 UTC (5,421 KB)
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