Human pose estimation: Datasets & Models
Human pose estimation uses keypoint detection to locate body joints, shoulders, elbows, hips, knees, wrists, and ankles, in images and video. It powers fitness form tracking, rep counting, and body-driven interactions in AR applications.
Human pose estimation
This workflow performs human pose estimation by detecting body keypoints such as shoulders, elbows, hips, knees, wrists, and ankles, then connecting them into a skeleton to track posture and movement across video frames. It accepts standard RGB images or video from phone cameras, webcams, or fixed cameras, and outputs coordinates for each detected joint that you can use for fitness form tracking, rep counting, or AR interactions.
Try it out
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Why automate human pose estimation
Manually annotating body movement or judging exercise form by eye doesn't scale and isn't repeatable, two coaches will grade the same squat differently, and no human can label thousands of workout frames. For fitness and AR builders, a keypoint model turns raw camera feeds into quantifiable motion data: you can count reps, flag poor form when a joint angle drifts outside range, or drive an avatar's limbs in real time. Starting from a human pose dataset lets you train or fine-tune a model against the specific angles, lighting, and body positions your app actually sees. That removes guesswork and gives you consistent, frame-accurate measurements.
Key benefits
Keypoint detection runs on the cameras and phones users already have, so there's no special hardware to deploy. You get consistent, structured joint data you can measure against thresholds, enabling automated rep counting, form correction, and responsive AR overlays, and the same pipeline scales from a single prototype to thousands of concurrent users. Building on an existing human pose dataset shortens the path from idea to a working, testable model.
How to use it
- Upload an image: Drop any photo into the widget above to try human pose estimation for free, right on this page.
- Preview instant results: The model detects person, head, hand, knee, foot within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
- Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train Keypoint detection on your own images for production accuracy.
- Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.
Recommended model for human pose estimation
When you're ready to build a production model for human pose estimation, we recommend training Keypoint detection. Unlike the ready-to-use model in the demo above, you fine-tune it on your own labeled images for higher accuracy, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Human Pose Estimation project to Roboflow Agent.