Pose Estimation Models

A curated collection of state-of-the-art open-source models for detecting and analyzing human body keypoints in real time.

What Is Pose Estimation?

Pose estimation is a computer vision technique that detects the position and orientation of a person's body parts — such as joints (elbows, knees, shoulders) and limbs — from an image or video. It forms the backbone of applications ranging from fitness tracking and augmented reality to autonomous driving and human-computer interaction. Below is a hand-picked selection of the most impactful open-source models available today.

OpenPose

The first real-time multi-person system to jointly detect human body, hand, facial, and foot keypoints. Developed at Carnegie Mellon University, it remains a foundational benchmark in the field.

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MoveNet

An ultra fast and accurate pose detection model built by Google. Available in Lightning and Thunder variants, it is optimized for mobile and edge devices with exceptional real-time performance.

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TransPose

A novel human pose estimation model combining a CNN feature extractor with a Transformer Encoder and a dedicated prediction head, leveraging attention mechanisms for superior keypoint localization.

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DensePose

Dense human pose estimation goes beyond keypoints by mapping every human pixel of an RGB image to a 3D surface of the body, enabling dense correspondence between images and the human body model.

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Lightweight OpenPose

A lightweight implementation of OpenPose that enables real-time 2D multi-person pose estimation directly on CPU, making it accessible for deployment in resource-constrained environments.

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HigherHRNet

Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation. It addresses the challenge of detecting keypoints at various scales by maintaining high-resolution representations throughout the network.

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DCPose

Deep Dual Consecutive Network for Human Pose Estimation. This architecture employs a dual-consecutive design to progressively refine keypoint predictions for enhanced accuracy.

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