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MediaPipe Pose

  1. Overview
  2. ML Pipeline
  3. Models
    1. Pose Detection Model (BlazePose Detector)
    2. Pose Landmark Model (BlazePose Tracker)
  4. Example Apps
    1. Mobile
    2. Desktop
    3. Python
    4. Web
  5. Resources


Human pose estimation from video plays a critical role in various applications such as quantifying physical exercises, sign language recognition, and full-body gesture control. For example, it can form the basis for yoga, dance, and fitness applications. It can also enable the overlay of digital content and information on top of the physical world in augmented reality.

MediaPipe Pose is a ML solution for high-fidelity upper-body pose tracking, inferring 25 2D upper-body landmarks from RGB video frames utilizing our BlazePose research. Current state-of-the-art approaches rely primarily on powerful desktop environments for inference, whereas our method achieves real-time performance on most modern mobile phones, desktops/laptops, in python and even on the web. A variant of MediaPipe Pose that performs full-body pose tracking on mobile phones will be included in an upcoming release of ML Kit.

Fig 1. Example of MediaPipe Pose for upper-body pose tracking.

ML Pipeline

The solution utilizes a two-step detector-tracker ML pipeline, proven to be effective in our MediaPipe Hands and MediaPipe Face Mesh solutions. Using a detector, the pipeline first locates the pose region-of-interest (ROI) within the frame. The tracker subsequently predicts the pose landmarks within the ROI using the ROI-cropped frame as input. Note that for video use cases the detector is invoked only as needed, i.e., for the very first frame and when the tracker could no longer identify body pose presence in the previous frame. For other frames the pipeline simply derives the ROI from the previous frame’s pose landmarks.

The pipeline is implemented as a MediaPipe graph that uses a pose landmark subgraph from the pose landmark module and renders using a dedicated upper-body pose renderer subgraph. The pose landmark subgraph internally uses a pose detection subgraph from the pose detection module.

Note: To visualize a graph, copy the graph and paste it into MediaPipe Visualizer. For more information on how to visualize its associated subgraphs, please see visualizer documentation.


Pose Detection Model (BlazePose Detector)

The detector is inspired by our own lightweight BlazeFace model, used in MediaPipe Face Detection, as a proxy for a person detector. It explicitly predicts two additional virtual keypoints that firmly describe the human body center, rotation and scale as a circle. Inspired by Leonardo’s Vitruvian man, we predict the midpoint of a person’s hips, the radius of a circle circumscribing the whole person, and the incline angle of the line connecting the shoulder and hip midpoints.

Fig 2. Vitruvian man aligned via two virtual keypoints predicted by BlazePose detector in addition to the face bounding box.

Pose Landmark Model (BlazePose Tracker)

The landmark model currently included in MediaPipe Pose predicts the location of 25 upper-body landmarks (see figure below), each with (x, y, z, visibility). Note that the z value should be discarded as the model is currently not fully trained to predict depth, but this is something we have on the roadmap. The model shares the same architecture as the full-body version that predicts 33 landmarks, described in more detail in the BlazePose Google AI Blog and in this paper.

Fig 3. 25 upper-body pose landmarks.

Example Apps

Please first see general instructions for Android, iOS, desktop and Python on how to build MediaPipe examples.

Note: To visualize a graph, copy the graph and paste it into MediaPipe Visualizer. For more information on how to visualize its associated subgraphs, please see visualizer documentation.



Please first see general instructions for desktop on how to build MediaPipe examples.


MediaPipe Python package is available on PyPI, and can be installed simply by pip install mediapipe on Linux and macOS, as described below and in this colab. If you do need to build the Python package from source, see additional instructions.

Activate a Python virtual environment:

$ python3 -m venv mp_env && source mp_env/bin/activate

Install MediaPipe Python package:

(mp_env)$ pip install mediapipe

Run the following Python code:

import cv2
import mediapipe as mp
mp_drawing =
mp_pose =

# For static images:
pose = mp_pose.Pose(
    static_image_mode=True, min_detection_confidence=0.5)
for idx, file in enumerate(file_list):
  image = cv2.imread(file)
  # Convert the BGR image to RGB before processing.
  results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

  # Print and draw pose landmarks on the image.
      'nose landmark:',
  annotated_image = image.copy()
      annotated_image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
  cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', image)

# For webcam input:
pose = mp_pose.Pose(
    min_detection_confidence=0.5, min_tracking_confidence=0.5)
cap = cv2.VideoCapture(0)
while cap.isOpened():
  success, image =
  if not success:

  # Flip the image horizontally for a later selfie-view display, and convert
  # the BGR image to RGB.
  image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
  # To improve performance, optionally mark the image as not writeable to
  # pass by reference.
  image.flags.writeable = False
  results = pose.process(image)

  # Draw the pose annotation on the image.
  image.flags.writeable = True
  image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
      image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
  cv2.imshow('MediaPipe Pose', image)
  if cv2.waitKey(5) & 0xFF == 27:

Tip: Use command deactivate to exit the Python virtual environment.


Please refer to these instructions.