Multi-Constraint 3D Human Pose Refinement via Kinematic–Temporal Optimization

Three-dimensional (3D) human pose sequences often exhibit coordinate noise, inconsistent bone lengths, and temporal jitter, which cannot be adequately assessed using positional error alone. We propose an offline kinematic–temporal refinement framework integrating reconstruction fidelity, bone-length consistency, multi-order temporal regularization, bilateral segment-length symmetry, and a soft joint range-of-motion constraint. The framework optimizes 3D joint coordinates without using reference poses during optimization and employs matched-weight ablations and progress-dependent weight scheduling. Experiments cover 371 non-overlapping 64-frame windows from two Human3.6M test subjects, using five realizations of independent Gaussian, temporally correlated, and limb-coherent synthetic perturbations. Under independent Gaussian noise, Full-Fixed refinement reduces MPJPE from 23.96 to 10.34 mm and bone-length error from 16.95 to 3.19 mm. MPJPE also decreases from 23.94 to 10.32 mm under limb-coherent perturbations and from 23.91 to 19.05 mm under temporally correlated perturbations. These results show that temporal correlation makes positional correction more challenging, while kinematic constraints improve skeletal consistency with noise-dependent positional trade-offs. Weight scheduling further adjusts temporal errors with small changes in positional accuracy. Overall, the framework enables interpretable offline refinement with explicitly characterized accuracy–structure–motion trade-offs.