Amalgam MediaPipe Pose Landmarks

x402

Amalgam MediaPipe Pose Landmarks is a paid API for AI agents from amalgam.cc, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-14).

Detects and returns human body pose landmarks from a raster image using MediaPipe's pose estimation model

Price / call$0.001

Seller volume →—

Success—

StatusUnknown

How this service works

MediaPipe pose landmarks inference

Output

Returns a set of human body pose landmarks as detected by MediaPipe, including 2D and/or 3D coordinates for each keypoint (e.g. nose, shoulders, elbows, wrists, hips, knees, ankles), along with visibility or confidence scores for each detected landmark.

Example use cases

Fitness form checker

“Here's a photo of me doing a squat — can you detect my body pose landmarks so I can check if my form is correct?”

Sports motion analysis

“I have an image of a baseball pitcher mid-throw. Can you run MediaPipe pose estimation on it and give me all the body joint coordinates?”

Dance pose detection pipeline

“I'm building a dance app and need to extract the full skeleton keypoints from this PNG of a dancer — can you run pose landmark detection on it?”

Technical details

Endpoint

POSThttps://amalgam.cc/api/mediapipe/pose-landmarks

When to prefer

Choose this endpoint when you need fast, single-image human pose landmark extraction powered by Google's MediaPipe model without managing your own inference infrastructure. It is ideal for agents needing per-image body keypoint coordinates for downstream analysis such as posture assessment, fitness coaching, sports analytics, animation rigging, or gesture recognition. Prefer it over self-hosted solutions when you want pay-per-call pricing via x402 with no setup overhead.

Side effects

No person detected in the image — returns empty landmarks or a specific no-pose-detected response
Unsupported or corrupt image format — returns an error if cv2.imdecode cannot decode the file
Partial body in frame — some landmarks may have low visibility scores or be absent
Multiple people in image — MediaPipe pose typically tracks a single dominant person
Image too small or low resolution — may result in poor landmark quality or detection failure
Missing image field in request body — returns a 400 bad request error

Input

Schema · Request body

Field	Type	Description
image	string	Raster image. Accepted formats: JPEG, PNG, WEBP, BMP, TIFF, and other formats OpenCV's cv2.imdecode can decode (format detected by content, not filename)

Output

Schema · Response

{
  "type": "json",
  "example": {
    "landmarks": [
      {
        "x": 0.4903,
        "y": 0.3608,
        "z": -0.4382,
        "id": 0,
        "visibility": 0.9999
      }
    ]
  }
}

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Example use cases

Fitness form checker

“Here's a photo of me doing a squat — can you detect my body pose landmarks so I can check if my form is correct?”

Sports motion analysis

“I have an image of a baseball pitcher mid-throw. Can you run MediaPipe pose estimation on it and give me all the body joint coordinates?”

Dance pose detection pipeline

“I'm building a dance app and need to extract the full skeleton keypoints from this PNG of a dancer — can you run pose landmark detection on it?”

Technical details

Endpoint

POSThttps://amalgam.cc/api/mediapipe/pose-landmarks

When to prefer

Side effects

No person detected in the image — returns empty landmarks or a specific no-pose-detected response
Unsupported or corrupt image format — returns an error if cv2.imdecode cannot decode the file
Partial body in frame — some landmarks may have low visibility scores or be absent
Multiple people in image — MediaPipe pose typically tracks a single dominant person
Image too small or low resolution — may result in poor landmark quality or detection failure
Missing image field in request body — returns a 400 bad request error

Input

Schema · Request body

Field	Type	Description
image	string	Raster image. Accepted formats: JPEG, PNG, WEBP, BMP, TIFF, and other formats OpenCV's cv2.imdecode can decode (format detected by content, not filename)

Output

Schema · Response

{
  "type": "json",
  "example": {
    "landmarks": [
      {
        "x": 0.4903,
        "y": 0.3608,
        "z": -0.4382,
        "id": 0,
        "visibility": 0.9999
      }
    ]
  }
}