Question 1

Which keypoints are detected?

Accepted Answer

The MoveNet 17-keypoint set: nose, left/right eye, left/right ear, left/right shoulder, left/right elbow, left/right wrist, left/right hip, left/right knee, left/right ankle. Each keypoint comes with an x/y position in image pixels plus a confidence score 0..1.

Question 2

How accurate is it?

Accepted Answer

Strong on clear single-person photos at any angle. The Lightning variant is optimised for speed at a small accuracy cost vs the heavier Thunder variant — it lands every clearly visible keypoint, but partially occluded limbs (hands behind back, ankles cropped out of frame) are sometimes missed and the confidence score reflects that.

Question 3

Does it find multiple people in one frame?

Accepted Answer

The single-pose variant ships first because it covers the most common use case (a sports / fitness / dance photo with one main subject). A multi-pose variant covering up to six people will follow once the larger model is uploaded to the asset CDN.

Question 4

What outputs do I get?

Accepted Answer

Two files: the original image with a skeleton drawn over the detected keypoints (PNG), and a separate JSON file with the raw keypoint coordinates and scores so downstream tools (sports analytics, fitness apps, animation rigs) can consume them.

Question 5

Will my photo upload?

Accepted Answer

No. MoveNet runs entirely on ONNX Runtime Web inside your browser. The model file (~4 MB) downloads once and is cached locally; everything else stays in your tab.

Question 6

Does it find every body part?

Accepted Answer

It finds the 17 keypoints in the COCO standard set: nose, left/right eye, left/right ear, left/right shoulder, left/right elbow, left/right wrist, left/right hip, left/right knee, left/right ankle. Hands (individual fingers), feet (individual toes), and facial landmarks beyond eyes/ears/nose are out of scope for this model — those need separate hand-tracking and face-landmark networks.

Question 7

How accurate is it?

Accepted Answer

Strong on clear single-person photos at any angle. The Lightning variant is optimised for speed at a small accuracy cost compared with the heavier Thunder variant — every clearly visible keypoint lands cleanly, but partially occluded limbs (hands behind back, ankles cropped out of frame) sometimes return low-confidence positions. Raise the confidence threshold to 0.5 to hide low-confidence detections from the overlay.

Question 8

Does it handle multiple people?

Accepted Answer

The single-pose variant ships first — it returns one set of keypoints for the most prominent subject. A multi-pose variant covering up to six people will follow once the larger model is uploaded to the asset CDN. For now, crop the source to one person at a time with Crop Image and run each cropped subject separately.

Question 9

Will my photo be uploaded?

Accepted Answer

No. MoveNet runs entirely on ONNX Runtime Web inside your browser. The model file (~4 MB) downloads once from our CDN and is cached locally; everything else stays in your tab.

Question 10

What if no person is in the photo?

Accepted Answer

MoveNet returns 17 keypoints with low confidence scores. The default threshold of 0.3 filters most spurious detections from the skeleton overlay. The JSON output retains every keypoint with its score so downstream code can decide what to do with low-confidence data.

Question 11

How fast is it on a phone?

Accepted Answer

Sub-second on a recent iPhone or flagship Android after the initial model load. The Lightning variant was specifically designed for mobile real-time inference, so a single still photo finishes in well under the time it takes to decode and display the result.

Question 12

Can I use the keypoint JSON in my own tool?

Accepted Answer

Yes — the JSON is a standard structure: an object with source dimensions, model input size, and a keypoints array where each entry has name, x, y, and score. Drop it into any downstream pipeline that consumes COCO-style keypoint data.

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