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Real-Time Human Pose Estimation — 17 Keypoints Per Person, Rendered as an OpenPose-Style Skeleton

A camera sees a person as pixels, not as a body with joints that bend. RF-DETR Keypoint reads a photo and, for every person in it, pins down exactly where the joints are — the eyes, shoulders, elbows, wrists, hips, knees, and ankles — the 17 standard COCO keypoints. It hands back both a ready-to-use OpenPose-style skeleton image and a precise text list of coordinates, fast enough to run on live video. Built by Roboflow on its RF-DETR real-time transformer family. This version is marked “Preview” — a work in progress that will keep improving.

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What it does

RF-DETR Keypoint looks at a photo and, for each person it finds, returns where the joints of that person are. Specifically, it predicts the 17 standard COCO keypoints: nose, left eye, right eye, left ear, right ear, left shoulder, right shoulder, left elbow, right elbow, left wrist, right wrist, left hip, right hip, left knee, right knee, left ankle, and right ankle. The node returns two things at the same time: a skeleton image (a black canvas with colored bones connecting the joints, in the well-known OpenPose visual style — red/orange arms, yellow/green legs, purple face) and a text list of every detection with pixel coordinates and confidence scores. This version is marked "Preview" — it's a work-in-progress addition to the RF-DETR lineup.

Problem it solves

• See what pose someone is in – Get the exact location of every joint (eyes, shoulders, elbows, wrists, hips, knees, ankles) for every person in a photo
• Drive character animation / motion transfer – The OpenPose-format skeleton map is the standard input for pose-guided image generation, ControlNet Pose, or motion transfer pipelines
• Sports / fitness analytics – Measure body posture, form, movement — golf swing, yoga pose, running form, weightlifting stance
• Health & rehab tracking – Monitor range of motion, symmetry, gait, posture over time
• Photo/video composition – Understand where the people are and how they're arranged in a scene
• Multi-person scenes – Returns a separate skeleton for each person in the image
• Real-time performance – Same fast transformer backbone as RF-DETR detection/segmentation, so it's fast enough for live video

Input/Output

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• Input: A regular color photo (PNG / JPG / JPEG / WebP) </aside>

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• Options:
  • Minimum confidence score to keep a detected person (0.0 – 1.0, default 0.5)
    • Only people the model is at least this confident about are returned
    • Higher = fewer, more confident detections; lower = more people kept, including weaker guesses </aside>

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• Output: Two outputs, in parallel:

  • Skeleton image — a rendered picture on a black background showing every detected person as an OpenPose-format stick figure: colored bones connecting the 17 joints, with joint dots. Ready to feed into any downstream node that expects an OpenPose-format pose map (e.g., pose-guided image generation, ControlNet Pose)

  • Text list — one line per detected element:

    • Person line — person,(x1 y1 x2 y2),score — the person's bounding box and detection score
    • Keypoint lines — one per joint, <joint_name>,(x y),confidence — e.g. nose,(223.74 276.33),0.998, left_eye,(260.49 246.91),0.999, left_wrist,(410.11 522.4),0.87, etc.
    • Joints in order: nose, left_eye, right_eye, left_ear, right_ear, left_shoulder, right_shoulder, left_elbow, right_elbow, left_wrist, right_wrist, left_hip, right_hip, left_knee, right_knee, left_ankle, right_ankle

    

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Accuracy & Speed

How RF-DETR Keypoint compares

RF-DETR Keypoint shares the same real-time transformer backbone as its siblings in the studio — RF-DETR Medium (object detection) and RF-DETR Seg Medium (instance segmentation) — but adds a keypoint prediction head so it returns human joint locations instead of boxes or masks. Choose this one when you need pose skeletons for animation, sports/rehab analytics, or ControlNet-style pose guidance; use the detection or segmentation siblings for boxes or masks.