Pose Estimation
Finding the joints of a body in an image — solved well enough to be boring, and the applications are mostly about watching people.
Reviewed July 14, 2026Stable
When not to use it
- As anonymous data. Gait is identifying. People are recognisable from how they move, at distance, without a face.
- Through occlusion. Crossed arms and crowds produce confident nonsense.
- For 3D, from one camera, expecting accuracy. The depth ambiguity is the same projective problem.
- On atypical bodies. The learned prior is "people bend like this," and gymnasts, dancers and wheelchair users break it.
Reach for something else instead
- Marker-based mocap — accurate, expensive, studio-bound.
- IMU sensors — wearables. No cameras, no occlusion.
- Depth cameras — resolves the 3D ambiguity with a measurement.
- Object detection — if you only need to know a person is there, don't take their skeleton.
Further reading
- Cao et al. (2019), OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields — the method that made multi-person real-time work.
- Loper et al. (2015), SMPL: A Skinned Multi-Person Linear Model — the parametric body model everything 3D uses.
- Andriluka et al. (2014), 2D Human Pose Estimation: New Benchmark and State of the Art Analysis — MPII; the benchmark that drove the field.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Claiming skeletons are anonymous. Gait recognition exists; that claim is doing a lot of work in surveillance contracts.
- Regressing joint coordinates directly. Heatmaps are better — spatial output for a spatial problem.
- Expecting 3D lifting to be reliable. Infinitely many 3D poses project to the same 2D skeleton.
- Ignoring who's in the training data. Less studied than face recognition, same shape of problem.
At a glance
FieldComputer Vision
What it givesa skeleton: 17-25 joints, real-time, multi-person
The elegant bitPart Affinity Fields make crowd assignment polynomial
The privacy claim"it's just a skeleton" is doing a lot of work; gait is identifying
Where it livesa regulatory gap its growth market depends on
DifficultyIntermediate
Often compared with
Pose estimation vs. face recognition — one tells you who someone is and is regulated; the other tells you what they're doing and mostly isn't.
Where this sits
A destination. 11 concepts lead here, and nothing in the corpus depends on it.
8Levelsteps in
11Needs firstconcepts
0Opens upnothing further
1Areastays here
Learn these firstObject Detection
Computed from the prerequisite graph, not assigned. How this works