Pose Estimation

Pose estimation is a computer vision technique that predicts the configuration of a person's or object's joints or parts in an image or video.

It involves detecting and tracking the position and orientation of these parts, usually represented as keypoints.

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.

Pose Estimation Related Models[edit]

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	MediaPipe GitHub	Apache 2.0
2	Detectron2	Facebook AI Research	High-performance codebase for object detection and segmentation, including pose estimation	Detectron2 GitHub	Apache 2.0
3	OpenPose	Carnegie Mellon University	Detecting key points of the human body, including hand, facial, and foot	OpenPose GitHub	MIT
4	MoveNet	Google Research	Detecting 17 critical key points of the human body	MoveNet GitHub	Apache 2.0
5	PoseNet	Google Research	Detecting different body parts, providing comprehensive skeletal information	PoseNet GitHub	Apache 2.0
6	DCPose	Deep Dual Consecutive Network	Detecting human pose from multiple frames, addressing motion blur and occlusions	DCPose GitHub	MIT
7	DensePose	Facebook AI Research	Mapping human-based pixels from an RGB image to the 3D surface of a human body	DensePose GitHub	Apache 2.0
8	HigherHRNet	HRNet	Addressing scaling differences in pose prediction, especially for shorter people	HigherHRNet GitHub	MIT
9	Lightweight OpenPose	Daniil-Osokin	Real-time inference with minimal accuracy drop, detecting human poses through key points	Lightweight OpenPose GitHub	MIT
10	AlphaPose	MVIG-SJTU	Detecting multiple individuals in various scenes, achieving high mAP on COCO and MPII datasets	AlphaPose GitHub	MIT