Pose Estimation MCQ
Locate body joints in images—single or crowded scenes—with heatmaps, associations, or top-down detectors.
Joints
K keypoints
Heatmap
Per joint
Multi-person
Parse
PAFs / graph
Link limbs
Human pose from pixels
Pose estimation predicts 2D (or 3D) locations of anatomical joints. Top-down methods detect people then estimate pose per crop; bottom-up methods predict all joints then group them (e.g. Part Affinity Fields). Datasets like COCO define a standard skeleton and metrics (OKS).
OKS / PCK
Object Keypoint Similarity generalizes AP to keypoints using scale-normalized distance thresholds.
Key ideas
Keypoints
Wrist, elbow, hip, etc., as (x,y) or heatmap peaks.
Heatmap head
One channel per joint; argmax or refinement for location.
Top-down
Person detector → single-person pose network per box.
Bottom-up
All joints + pairwise cues to assemble instances.
Typical stack
backbone → heatmaps / offsets → grouping or single-person decode