MediaPipe Vs OpenCV

MediaPipe Vs OpenCV: A Comparison of Pose Estimation Tools

MediaPipe and OpenCV are key players in computer vision. MediaPipe, by Google, shines in VR, AR, human-computer interaction, and sign language recognition. It’s great for real-time applications like hand tracking and face detection, thanks to its efficient machine learning pipelines.

OpenCV is the go-to for image processing and manipulation, supporting a wide range of applications including motion tracking and pose estimation. It works across platforms like Windows, Linux, and macOS, making it versatile for handling video streams.

In summary, MediaPipe excels in quick, real-time machine learning tasks for live media, while OpenCV offers a broad toolkit for image-based computer vision projects. Both bring valuable tools to the table for anyone working in computer vision.

Key Features of MediaPipe and OpenCV:




Primary Focus

High accuracy, real-time performance, pre-built models.

Computer vision and image processing

Developed by


Community project (originally Intel)

Release Date



Programming Languages

C++, Python (with limited support)

C++, Python, Java, and more

Key Features

Pre-built ML solutions for various tasks (e.g., face detection, hand tracking)

Comprehensive set of computer vision functions (e.g., image transformations, object detection)


Cross-platform (mobile, desktop, web)

Cross-platform (mobile, desktop, web)

Community & Support

Growing community, with support mainly from Google and GitHub issues

Large and well-established community, extensive documentation, and many tutorials

Use Cases

Real-time applications, augmented reality, gesture recognition

Image processing, computer vision research, real-time applications, machine learning


Optimised for real-time applications on both mobile and desktop

Highly optimised, with support for multi-core processing and hardware acceleration


Customisable pipelines, but more limited compared to OpenCV

Highly customisable and extensible with support for third-party plugins and algorithms

  • 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.


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.

Add QuickPose with our Integration Team

Book a consultation to discuss your use case and capabilities.