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 | PCT (swin-l, test set) | PCKh-0.5 | 94.3 | — | Unverified |
| 2 | Soft-gated Skip Connections | PCKh-0.5 | 94.1 | — | Unverified |
| 3 | Cascade Feature Aggregation | PCKh-0.5 | 93.9 | — | Unverified |
| 4 | PCT (swin-b, test set) | PCKh-0.5 | 93.8 | — | Unverified |
| 5 | TransPose | PCKh-0.5 | 93.5 | — | Unverified |
| 6 | UniHCP (FT) | PCKh-0.5 | 93.2 | — | Unverified |
| 7 | 4xRSN-50 | PCKh-0.5 | 93 | — | Unverified |
| 8 | UniPose | PCKh-0.5 | 92.7 | — | Unverified |
| 9 | MSPN | PCKh-0.5 | 92.6 | — | Unverified |
| 10 | Spatial Context | PCKh-0.5 | 92.5 | — | Unverified |