Generate detailed pose estimates from images
Estimate 3D character pose from a sketch
Analyze workout posture in real-time
Estimate human poses in images
Transform pose in an image using another image
Play Solfeggio tones to enhance well-being
Create a video using aligned poses from an image and a dance video
Generate dance pose video from aligned pose
Synthpose Markerless MoCap VitPose
Visualize pose-format components and points.
Estimate camera poses from two images
Detect and estimate human poses in images
Detect and visualize poses in videos
OpenPose is a cutting-edge library for pose estimation developed by the Carnegie Mellon University (CMU) Perceptual Computing Laboratory. It is designed to detect and estimate human body poses from images, videos, and other media. OpenPose supports both 2D and 3D pose estimation and is widely used in fields like computer vision, robotics, and fitness tracking. It is an open-source tool, making it accessible for research and commercial use.
• Multi-person pose estimation: OpenPose can detect poses of multiple people in a single image. • Real-time processing: Enables fast and efficient pose estimation for real-time applications. • Body, face, and hands tracking: Supports estimation of body keypoints, facial landmarks, and hand keypoints. • 3D pose estimation: Offers 3D pose reconstruction for deeper understanding of human movement. • Cross-platform compatibility: Runs on Windows, Linux, and macOS. • Customizable models: Users can train or use pre-trained models for specific use cases.
What input formats does OpenPose support?
OpenPose supports images (JPG, PNG, etc.), videos (MP4, AVI, etc.), and camera inputs. It can also process frames from video streams.
Can OpenPose work with multiple people in the same image?
Yes, OpenPose is designed to detect and estimate poses of multiple people simultaneously in a single image or video frame.
How do I improve the accuracy of pose estimation?
You can improve accuracy by using higher-resolution images, adjusting model parameters, or re-training the model with a dataset specific to your use case.