  Current approaches in pose estimation primarily concentrate on enhancing
model architectures, often overlooking the importance of comprehensively
understanding the rationale behind model decisions. In this paper, we propose
XPose, a novel framework that incorporates Explainable AI (XAI) principles into
pose estimation. This integration aims to elucidate the individual contribution
of each keypoint to final prediction, thereby elevating the model's
transparency and interpretability. Conventional XAI techniques have
predominantly addressed tasks with single-target tasks like classification.
Additionally, the application of Shapley value, a common measure in XAI, to
pose estimation has been hindered by prohibitive computational demands.
  To address these challenges, this work introduces an innovative concept
called Group Shapley Value (GSV). This approach strategically organizes
keypoints into clusters based on their interdependencies. Within these
clusters, GSV meticulously calculates Shapley value for keypoints, while for
inter-cluster keypoints, it opts for a more holistic group-level valuation.
This dual-level computation framework meticulously assesses keypoint
contributions to the final outcome, optimizing computational efficiency.
Building on the insights into keypoint interactions, we devise a novel data
augmentation technique known as Group-based Keypoint Removal (GKR). This method
ingeniously removes individual keypoints during training phases, deliberately
preserving those with strong mutual connections, thereby refining the model's
predictive prowess for non-visible keypoints. The empirical validation of GKR
across a spectrum of standard approaches attests to its efficacy. GKR's success
demonstrates how using Explainable AI (XAI) can directly enhance pose
estimation models.
