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Pose estimation is widely used in applications such as motion capture, human-computer interaction, augmented reality, and robotics. The process typically involves training machine learning models on large datasets of annotated images to accurately identify and locate the keypoints.
{| class="wikitable sortable"
! Sr No !! Model !! Developer !! Key Points !! Source !! License
|-
| 1 || MediaPipe || Google || Tracking 33 key points on the human body, offering cross-platform, customizable ML solutions || [https://github.com/google/mediapipe MediaPipe GitHub] || Apache 2.0|-| 2 || Detectron2 || Facebook AI Research || High-performance codebase for object detection and segmentation, including pose estimation || [https://github.com/facebookresearch/detectron2 Detectron2 GitHub] || Apache 2.0|-| 3 || OpenPose || Carnegie Mellon University || Detecting key points of the human body, including hand, facial, and foot || [https://github.com/CMU-Perceptual-Computing-Lab/openpose OpenPose GitHub] || MIT|-| 4 || MoveNet || Google Research || Detecting 17 critical key points of the human body || [https://github.com/tensorflow/tfjs-models/tree/master/posenet MoveNet GitHub] || Apache 2.0|-| 5 || PoseNet || Google Research || Detecting different body parts, providing comprehensive skeletal information || [https://github.com/tensorflow/tfjs-models/tree/master/posenet PoseNet GitHub] || Apache 2.0|-| 6 || DCPose || Deep Dual Consecutive Network || Detecting human pose from multiple frames, addressing motion blur and occlusions || [https://github.com/DeepDualConsecutivePose/dcpose DCPose GitHub] || MIT|-| 7 || DensePose || Facebook AI Research || Mapping human-based pixels from an RGB image to the 3D surface of a human body || [https://github.com/facebookresearch/DensePose DensePose GitHub] || Apache 2.0|-| 8 || HigherHRNet || HRNet || Addressing scaling differences in pose prediction, especially for shorter people || [https://github.com/HRNet/HigherHRNet HigherHRNet GitHub] || MIT|-| 9 || Lightweight OpenPose || Daniil-Osokin || Real-time inference with minimal accuracy drop, detecting human poses through key points || [https://github.com/Daniil-Osokin/lightweight-human-pose-estimation Lightweight OpenPose GitHub] || MIT|-| 10 || AlphaPose || MVIG-SJTU || Detecting multiple individuals in various scenes, achieving high mAP on COCO and MPII datasets || [https://github.com/MVIG-SJTU/AlphaPose AlphaPose GitHub] || MIT|}
[[File:Pose detection overlay.gif|}720px|thumb|Mediapipe Pose Detection]] <gallery>Pose_example1.png|About to StandPose_example2.png|Standing but error in leg detectionPose_example3.png|Foreground missedPose_example4.png|Hoodie</gallery> [[File:Poseoutput white orig.gif|720px|thumb|Mediapipe Pose Detection]]