Pose Estimation
Pose Estimation is a computer vision task where the goal is to detect the position and orientation of a person or an object. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. in case of Human Pose Estimation.
A common benchmark for this task is MPII Human Pose
( Image credit: Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose )
Papers
Showing 1–10 of 4228 papers
All datasetsCOCO test-devMPII Human PoseOCHumanLeeds Sports PosesCrowdPoseCOCO val2017AICCOCO (Common Objects in Context)InLocITOP front-viewJ-HMDBMPII Single Person
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | BUCTD-W48 (w/cond. input from PETR, and generative sampling) | AP | 78.5 | — | Unverified |
| 2 | ViTPose-G | AP | 78.3 | — | Unverified |
| 3 | BUCTD-W48 (w/cond. input from PETR) | AP | 76.7 | — | Unverified |
| 4 | SwinV2-L 1K-MIM | AP | 75.5 | — | Unverified |
| 5 | SwinV2-B 1K-MIM | AP | 74.9 | — | Unverified |
| 6 | BUCTD-W48 | AP | 72.9 | — | Unverified |
| 7 | OpenPifPaf | AP | 70.5 | — | Unverified |
| 8 | MIPNet (HRNet-W48) | AP | 70 | — | Unverified |
| 9 | KAPAO-L | AP | 68.9 | — | Unverified |
| 10 | KAPAO-M | AP | 67.1 | — | Unverified |