Computer vision/deep learning-based 3D human pose estimation methods aim to localize human joints from images and videos. Pose representation is normally limited to 3D joint positional/translational degrees of freedom (3DOFs), however, a further three rotational DOFs (6DOFs) are required for many potential biomechanical applications. Positional DOFs are insufficient to analytically solve for joint rotational DOFs in a 3D human skeletal model. Therefore, we propose a temporal inverse kinematics (IK) optimization technique to infer joint orientations throughout a biomechanically informed, and subject-specific kinematic chain. For this, we prescribe link directions from a position-based 3D pose estimate. Sequential least squares quadratic programming is used to solve a minimization problem that involves both frame-based pose terms, and a temporal term. The solution space is constrained using joint DOFs, and ranges of motion (ROMs). We generate 3D pose motion sequences to assess the IK approach both for general accuracy, and accuracy in boundary cases. Our temporal algorithm achieves 6DOF pose estimates with low Mean Per Joint Angular Separation (MPJAS) errors (3.7{\deg}/joint overall, & 1.6{\deg}/joint for lower limbs). With frame-by-frame IK we obtain low errors in the case of bent elbows and knees, however, motion sequences with phases of extended/straight limbs results in ambiguity in twist angle. With temporal IK, we reduce ambiguity for these poses, resulting in lower average errors.
翻译:3D 人造图象估计方法旨在将图像和视频显示的人际交点的模糊度本地化。 Pose 表示法通常限于 3D 联合位置/翻译自由度( 3DFs 3DFs), 但是, 许多潜在的生物机械应用还需要另外三个旋转的 DOF( 6DFs 6DFs ) 。 定位 DOF 不足以在3D 人类骨骼模型中为联合旋转 DOF 提供分析解析。 因此, 我们提出一个时间反动运动优化技术, 在整个生物机能了解的较低和特定主题的运动链中推导出联合方向。 为此, 我们从基于位置的 3D( 6DOF ) 设定了方向, 并用基于基于框架的 3DOF 设定了一个时间术语和时间术语的最小化问题。 解决方案空间受到使用联合 DOF 和运动范围( ROMs) 限制。 我们生成了运动序列序列, 来评估 IK 方法的一般准确性、 和边界案例中的准确性选择 。 。 我们的Lealalalal ASAA/ frodeal 6DA/ frideal 。