Vision-based 3D human motion capture from videos remains a challenge in computer vision. Traditional 3D pose estimation approaches often ignore the temporal consistency between frames, causing implausible and jittery motion. The emerging field of kinematics-based 3D motion capture addresses these issues by estimating the temporal transitioning between poses instead. A major drawback in current kinematics approaches is their reliance on Euler angles. Despite their simplicity, Euler angles suffer from discontinuity that leads to unstable motion reconstructions, especially in online settings where trajectory refinement is unavailable. Contrarily, quaternions have no discontinuity and can produce continuous transitions between poses. In this paper, we propose QuaMo, a novel Quaternion Motions method using quaternion differential equations (QDE) for human kinematics capture. We utilize the state-space model, an effective system for describing real-time kinematics estimations, with quaternion state and the QDE describing quaternion velocity. The corresponding angular acceleration is computed from a meta-PD controller with a novel acceleration enhancement that adaptively regulates the control signals as the human quickly changes to a new pose. Unlike previous work, our QDE is solved under the quaternion unit-sphere constraint that results in more accurate estimations. Experimental results show that our novel formulation of the QDE with acceleration enhancement accurately estimates 3D human kinematics with no discontinuity and minimal implausibilities. QuaMo outperforms comparable state-of-the-art methods on multiple datasets, namely Human3.6M, Fit3D, SportsPose and AIST. The code is available at https://github.com/cuongle1206/QuaMo

翻译：基于视频的视觉三维人体运动捕捉在计算机视觉领域仍具挑战性。传统的三维姿态估计方法常忽略帧间的时间一致性，导致运动不自然且抖动。新兴的基于运动学的三维运动捕捉方法通过估计姿态间的时间过渡来解决这些问题。当前运动学方法的一个主要缺陷在于其对欧拉角的依赖。尽管欧拉角简单易用，但其不连续性会导致运动重建不稳定，尤其是在无法进行轨迹优化的在线场景中。相比之下，四元数具有连续性，能够实现姿态间的平滑过渡。本文提出QuaMo，一种利用四元数微分方程进行人体运动学捕捉的新型四元数运动方法。我们采用状态空间模型这一描述实时运动学估计的有效系统，以四元数为状态量，并用四元数微分方程描述四元数速度。相应的角加速度通过元PD控制器计算，该控制器采用新型加速度增强机制，可在人体快速切换至新姿态时自适应调节控制信号。与先前工作不同，我们的四元数微分方程在四元数单位球约束下求解，从而获得更精确的估计结果。实验表明，我们提出的加速度增强型四元数微分方程新公式能够无间断且高度准确地估计三维人体运动学数据，极大减少了不合理运动现象。在Human3.6M、Fit3D、SportsPose和AIST等多个数据集上，QuaMo均优于同类先进方法。代码发布于https://github.com/cuongle1206/QuaMo