姿勢推定とキーポイント
関節点を求め、骨格を復元する
本ページの本文は英語で提供されています。タイトルと導入は日本語化されています。
この能力とは何か
Takes an image or video and outputs coordinates for a set of keypoints — shoulders, elbows, wrists, hips, knees, ankles — which join into a skeleton. It adds geometric structure on top of detection and returns coordinate sequences rather than classes. It works both on a single person and on many people, grouping points per individual.
技術的にどう実現するか
Two paradigms dominate: top-down detects each person first and regresses keypoints inside the box, accurate but slower as the crowd grows; bottom-up predicts all joints over the image at once and assembles points into individuals using part-affinity fields, with speed largely independent of headcount. Heatmap regression was long the standard, and direct coordinate regression with Transformer backbones has since matured.
代表的な製品
4関連する組織
代表的な用途
- Fitness and sports motion analysis
- Human–computer interaction and gesture control
- Motion capture and animation driving
- Hand and face tracking
どう評価するか
- PCK
- Share of keypoints falling within a radius of the truth
- OKS / keypoint mAP
- Detection average precision weighted by joint visibility
- MPJPE
- Mean joint position error in 3D pose, in millimetres
限界と難しさ
- Occlusion and crops drop keypoints, yet the model still fills in a plausible but wrong location
- Extreme poses — handstands, curled-up bodies — fall outside training and amplify error
- With mutual occlusion, limbs are stitched onto the wrong person