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Neural MoCon: Neural Motion Control for Physically Plausible Human Motion Capture
[article]
2022
arXiv
pre-print
Due to the visual ambiguity, purely kinematic formulations on monocular human motion capture are often physically incorrect, biomechanically implausible, and can not reconstruct accurate interactions. In this work, we focus on exploiting the high-precision and non-differentiable physics simulator to incorporate dynamical constraints in motion capture. Our key-idea is to use real physical supervisions to train a target pose distribution prior for sampling-based motion control to capture
arXiv:2203.14065v1
fatcat:jwc7bxqrirguliv3jmbuznqari