openpose and lightweight-human-pose-estimation.pytorch
The second tool is a lightweight, faster implementation of the first tool, making them competitors, with the second being an optimized alternative for specific use cases.
About openpose
CMU-Perceptual-Computing-Lab/openpose
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
This project helps you automatically detect and track human body, face, and hand movements in real-time from videos, webcams, or images. It takes visual input and outputs detailed skeletal or keypoint data (like elbow, nose, or fingertip positions) for multiple people. Researchers in human-computer interaction, sports scientists, or animators can use this to analyze or reproduce human motion.
About lightweight-human-pose-estimation.pytorch
Daniil-Osokin/lightweight-human-pose-estimation.pytorch
Fast and accurate human pose estimation in PyTorch. Contains implementation of "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose" paper.
This project helps computer vision practitioners and researchers analyze human movement by identifying key body points. It takes an image or video frame as input and outputs a 'skeleton' of each person, showing precise locations of ears, eyes, nose, neck, shoulders, elbows, wrists, hips, knees, and ankles. Anyone working on applications requiring real-time human pose understanding, like sports analysis, animation, or safety monitoring, would find this valuable.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work