MediaPipe Vs OpenPose

MediaPipe Vs OpenPose: A Comparison of Pose Estimation Tools

OpenPose excels in accuracy, detecting 25 precise keypoints. However, MediaPipe, a Google innovation from 2019, boasts speed, smoothly processing video frames in real-time even on less powerful devices. OpenPose employs a bottom-up approach, identifying body parts before assembling them into complete poses, while MediaPipe opts for a top-down strategy, detecting keypoints to predict poses. MediaPipe shines in user-friendliness and versatility, offering a straightforward API and multi-language support, ideal for quick integration across various platforms. Conversely, OpenPose demands a robust GPU for efficient operation and might lag, taking seconds to analyse a single image. These differences underline each framework’s unique strengths, guiding users to choose based on their project’s specific needs and goals.

Key Features of MediaPipe and OpenPose:

Feature

MediaPipe

OpenPose

Developed By

Google

Carnegie Mellon University (Open Pose) 

Open Source

Yes

Yes

Primary Use

Multi-purpose (face, hand, pose)

Human pose estimation

Language/Framework

C++, Python, JavaScript

C++, Python

Platform Compatibility

Cross-platform (iOS, Android, Web)

Cross-platform (Windows, Linux, macOS)

Performance

Optimised for mobile and web

High performance on desktop

Real-time Capabilities

Yes, designed for real-time use

Yes, with suitable hardware

Multi-person Detection

Yes

Yes

Pre-trained Models

Yes, various models available

Yes, several models for different accuracies and speeds

Customizability

High, with multiple components

Moderate, with options for fine-tuning

Community & Support

Large, backed by Google

Large, widespread academic and hobbyist use

MediaPIpe Logo Vs QuickPose for pose estimation

Compare MediaPipe with QuickPose

  • MediaPipe, by Google, offers basic pose estimation but requires significant user processing.
  • QuickPose enhances MediaPipe with pre-built features, simplifying app development.

Effortlessly Integrate Pose Estimation into Your Mobile Apps with QuickPose

Accurate Pose Estimation

Advanced algorithms to provide highly accurate pose estimation, ensuring that your users get the best possible experience.

Customisable Output

Allows you to tailor the output to fit your specific needs, making it easy to integrate into your existing systems and processes.

Fast Processing

Optimized for speed, so you can process camera feeds quickly and efficiently.

Scalable

Can handle a large volume of requests, so it can easily scale to meet the needs of your business

Pre Built Models

Save your time and resources, allowing you to focus on other aspects of your product development.

Open-Source Framework

Mediapipe is an open-source framework developed by Google for building cross-platform, multi-purpose machine learning solutions.

Add our QuickPose iOS SDK into your app in two ways

You can implement QuickPose yourself via our GitHub Repo or we can help you integrate it into your app. 
Image shows an athlete doing a deadlift with Pose Estimation Landmarks on her body by MediaPipe

How QuickPose can be used

Build yourself with our GitHub Repo

Integrate QuickPose using our GitHub Repository and our documentation.

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Add QuickPose with our Integration Team

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