  Human pose estimation is a key task in computer vision with various
applications such as activity recognition and interactive systems. However, the
lack of consistency in the annotated skeletons across different datasets poses
challenges in developing universally applicable models. To address this
challenge, we propose a novel approach integrating multi-teacher knowledge
distillation with a unified skeleton representation. Our networks are jointly
trained on the COCO and MPII datasets, containing 17 and 16 keypoints,
respectively. We demonstrate enhanced adaptability by predicting an extended
set of 21 keypoints, 4 (COCO) and 5 (MPII) more than original annotations,
improving cross-dataset generalization. Our joint models achieved an average
accuracy of 70.89 and 76.40, compared to 53.79 and 55.78 when trained on a
single dataset and evaluated on both. Moreover, we also evaluate all 21
predicted points by our two models by reporting an AP of 66.84 and 72.75 on the
Halpe dataset. This highlights the potential of our technique to address one of
the most pressing challenges in pose estimation research and application - the
inconsistency in skeletal annotations.
