MediaPipe-Pose-Estimation

MediaPipe-Pose-Estimation: Optimized for Qualcomm Devices

The MediaPipe Pose Landmark Detector is a machine learning pipeline that predicts bounding boxes and pose skeletons of the face, hands, and torso in an image.

This is based on the implementation of MediaPipe-Pose-Estimation found here. This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here.

Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up to run these models on a hosted Qualcomm® device.

Getting Started

There are two ways to deploy this model on your device:

Option 1: Download Pre-Exported Models

Below are pre-exported model assets ready for deployment.

For more device-specific assets and performance metrics, visit MediaPipe-Pose-Estimation on Qualcomm® AI Hub.

Option 2: Export with Custom Configurations

Use the Qualcomm® AI Hub Models Python library to compile and export the model with your own:

• Custom weights (e.g., fine-tuned checkpoints)
• Custom input shapes
• Target device and runtime configurations

This option is ideal if you need to customize the model beyond the default configuration provided here.

See our repository for MediaPipe-Pose-Estimation on GitHub for usage instructions.

Model Details

Model Type: Model_use_case.pose_estimation

Model Stats:

• Input resolution: 256x256
• Model size (pose_detector) (float): 3.14 MB
• Model size (pose_landmark_detector) (float): 12.9 MB
• Number of parameters (pose_detector): 815K
• Number of parameters (pose_landmark_detector): 3.36M

Performance Summary

License

• The license for the original implementation of MediaPipe-Pose-Estimation can be found here.

References

• BlazePose: On-device Real-time Body Pose tracking
• Source Model Implementation

Community

• Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
• For questions or feedback please reach out to us.