  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.
