Human Pose Estimation (HPE) is a way of capturing 2D and 3D human movements using labels and annotations to train computer vision models. It's a powerful approach for tracking, annotating, and estimating movement patterns in humans and animals. HPE uses sophisticated algorithms, such as trained Computer Vision models, plus accurate and detailed annotations and labels, to understand and track human movements in a fraction of a second. To achieve production-ready models, it's essential to use high-quality datasets, including free, open-source options like the 15 discussed in this article. These datasets can be used for various applications, such as healthcare, sports, retail, security, intelligence, and military settings. By leveraging these datasets, computer vision models can capture human pose estimation tasks with greater accuracy and precision, overcoming challenges like automatic facial detection "in the wild" and multi-person pose estimation in crowded environments. HPE is a crucial component of computer vision-based approaches to annotations, enabling the creation of skeleton-like outlines of the human body or keypoint representations of joints, movements, and facial features.