# CMU-Perceptual-Computing-Lab/openpose

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

Repository: https://github.com/CMU-Perceptual-Computing-Lab/openpose
Canonical: https://ross.abutalabs.com/products/openpose
Homepage: https://cmu-perceptual-computing-lab.github.io/openpose
Language: C++
License: NOASSERTION
License Family: other
Topics: openpose, computer-vision, machine-learning, cpp, caffe, opencv, human-pose-estimation, real-time, deep-learning, human-behavior-understanding, cvpr-2017, multi-person, foot-estimation, keypoints, face, pose, pose-estimation, human-pose, keypoint-detection, hand-estimation
Last push: 2024-08-03T01:59:11+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3418, "days_push": 761, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 34413, forks 8037 (observed 2026-08-28T04:12:00.589216+00:00)

## What it is
OpenPose is a real-time multi-person keypoint detection library that jointly estimates human body, face, hand, and foot keypoints (135 total) from single images or video. It is written in C++ with Python bindings and built on Caffe and OpenCV.

## Use cases
- detect human body keypoints in images or video in real time
- estimate facial landmarks and hand keypoints from photos
- track multiple people's poses in a video stream
- extract 2D pose data for action recognition or behavior analysis
- build motion capture or fitness form-checking applications
- annotate datasets with human pose keypoints

## When to choose
- you need real-time multi-person 2D pose estimation including face, hands, and feet
- you want a well-established, widely cited pose estimation library with C++ and Python APIs
- you need to run pose estimation on CPU or NVIDIA GPUs across Linux, macOS, and Windows

## When to avoid
- you need 3D pose estimation or state-of-the-art transformer-based accuracy
- you want a lightweight pure-Python or pip-installable solution
- your project requires a permissively licensed library, since OpenPose uses a non-commercial license
- you need actively developed features, as the project is largely in maintenance mode

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, machine-learning, deep-learning, image-processing
- domain: computer-vision, machine-learning, deep-learning, image-processing
- platform: windows, cpp, python
- tags: pose-estimation, keypoint-detection, human-pose, real-time, multi-person, face-landmarks, hand-tracking, caffe, opencv, linux, macos, gpu

## Member repositories
- CMU-Perceptual-Computing-Lab/openpose (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:00.589216+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:27:34.397041+00:00, confidence not recorded.
  - readme: https://github.com/CMU-Perceptual-Computing-Lab/openpose (fetched 2026-08-28T04:12:00.589216+00:00, sha f8e22e91869c)
  - homepage: https://cmu-perceptual-computing-lab.github.io/openpose (fetched 2026-08-29T07:48:15.609363+00:00, sha f7cdeacb405d)
- Data as of 2026-08-30T08:39:29.467469+00:00.