Pose Estimation Annotation

The pose estimation annotation process involves identifying and labeling specific anatomical keypoints—such as joints, limbs, and facial features—on an image to map the structure of a target object, usually a human or animal. Annotators typically select predefined landmarks based on a standard skeletal template (like COCO or MediaPipe) and place them on the corresponding locations within the target, often connecting these points with edges to represent the object's kinematic chain. This hierarchical process transforms a two-dimensional image into a structured graph, enabling machine learning models to infer not just the presence of a subject, but their precise posture, movement trajectory, and intended action over time.