XPose: eXplainable Human Pose Estimation

Pose estimation models often lack transparency: Current AI systems for human pose estimation focus on improving accuracy but don't adequately explain why they make specific predictions, making it difficult to trust or debug these systems in real-world applications.

New explainability method makes AI more practical: XPose introduces "Group Shapley Value" (GSV), which identifies which body keypoints (joints) are most important to the model's predictions while being computationally feasible—solving a previous limitation where standard explanation techniques were too slow to apply to pose estimation.

Understanding keypoint relationships improves robustness: By analyzing how different body joints interact and depend on each other, the framework reveals that keypoints don't contribute equally or independently to predictions, enabling better model design and more reliable performance.

Explainability enables better training strategies: The research demonstrates that understanding keypoint contributions led to a new training technique (Group-based Keypoint Removal) that makes models more robust by strategically removing related keypoints during training, improving real-world reliability.

Responsible AI requires both accuracy and interpretability: This work emphasizes that responsible deployment of pose estimation systems requires not just accurate predictions, but also clear explanations of how models reach their conclusions—essential for applications in healthcare, safety, and other high-stakes domains.