Pose Estimation Models
Browse AI models for pose estimation in alphabetical order
DWPose
Evaluate DWPose as a pose preprocessor: separate detection, keypoint mapping, rendered pose maps and downstream generation before judging a character-reference workflow.
OpenPose
OpenPose is the pioneering real-time multi-person pose estimation system developed at Carnegie Mellon University that simultaneously detects body, face, hand, and foot keypoints of multiple people in images and videos. As the first open-source system to achieve real-time multi-person pose detection, OpenPose has become a foundational tool in computer vision research and creative AI applications. Built on a CNN (Convolutional Neural Network) architecture with approximately 25 million parameters, the model uses Part Affinity Fields (PAFs) to associate detected body parts with the correct individuals in crowded scenes, enabling accurate pose estimation even when people overlap or partially occlude each other. OpenPose detects up to 135 keypoints per person covering the full body skeleton with 25 points, each hand with 21 points, and the face with 70 points, providing comprehensive pose information for detailed motion analysis. The system processes both images and video streams, delivering real-time performance on modern GPUs that makes it suitable for interactive applications. OpenPose has been extensively integrated into AI image generation workflows, particularly as the standard pose extraction method for ControlNet conditioning in Stable Diffusion and FLUX-based generation pipelines. Released under a custom non-commercial license, the source code is available on GitHub and has accumulated one of the highest star counts among computer vision repositories. Key applications include motion capture for animation and gaming, fitness and rehabilitation tracking, sports biomechanics analysis, sign language recognition, dance analysis, human-computer interaction research, and providing pose conditioning for AI image generation tools.