论文标题
神经Mocon：身体上合理的人类运动捕获的神经运动控制
Neural MoCon: Neural Motion Control for Physically Plausible Human Motion Capture
论文作者
论文摘要
由于视觉上的歧义，纯粹的运动捕获中的运动学表述通常在物理上是不正确的，在生物力学上难以置信的，并且无法重建准确的相互作用。在这项工作中，我们专注于利用高精度和非差异物理模拟器以将动态约束纳入运动捕获中。我们的钥匙想法是使用实际的身体监督在基于抽样的运动控制之前先训练目标姿势分布，以捕获物理上合理的人类运动。为了获得与采样的地形相互作用的准确参考运动，我们首先引入基于SDF（签名距离场）的相互作用约束，以实施适当的地面接触模型。然后，我们设计了一种新型的两分支解码器，以避免伪真相的随机误差，并在不可差的物理模拟器之前训练分布。最后，我们使用训练有素的先验和样本满足目标姿势从物理特征的当前状态中回归采样分布，以跟踪估计的参考运动。定性和定量结果表明，我们可以通过复杂的地形相互作用，人形变化和多种行为获得物理上合理的人类运动。更多信息可以在〜\ url {https://www.yangangwang.com/papers/hbz-nm-2022-03.html}中找到
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 physically plausible human motion. To obtain accurate reference motion with terrain interactions for the sampling, we first introduce an interaction constraint based on SDF (Signed Distance Field) to enforce appropriate ground contact modeling. We then design a novel two-branch decoder to avoid stochastic error from pseudo ground-truth and train a distribution prior with the non-differentiable physics simulator. Finally, we regress the sampling distribution from the current state of the physical character with the trained prior and sample satisfied target poses to track the estimated reference motion. Qualitative and quantitative results show that we can obtain physically plausible human motion with complex terrain interactions, human shape variations, and diverse behaviors. More information can be found at~\url{https://www.yangangwang.com/papers/HBZ-NM-2022-03.html}
