MediaPipe Vs Tensorflow

MediaPipe Vs Tensorflow: A Comparison of Pose Estimation Tools

MediaPipe pipelines utilise TensorFlow models for on-device processing. MediaPipe focuses on building real-time applications with optimised models for on-device inference, while TensorFlow is a versatile machine learning framework for training and deploying various models, including those used by MediaPipe.

In essence, MediaPipe and TensorFlow can complement each other in various ways, with MediaPipe offering quick deployment and versatility, while TensorFlow caters to scalability, customisation, and long-term support. 

Key Features of MediaPipe and TensorFlow:




Primary Focus

Real-time, on-device machine learning pipelines

General machine learning and deep learning

Use Cases

Pose Estimation, face detection and hand tracking.

Wide range of ML tasks, including computer vision, NLP, and predictive analytics

Platform Support

Cross-platform (Android, iOS, web, and more)

Cross-platform (with a strong focus on server and cloud environments)


Optimised for real-time applications on edge devices

Scalable from small to very large models, optimised for high performance on both CPUs and GPUs

Pre-built Models

Offers pre-built solutions for common tasks like face detection, hand tracking, etc.

Extensive model zoo for various tasks, but requires more configuration


Limited to the pipeline components and pre-built models

Highly customizable, with support for custom layers, models, and training loops

Community & Support

Growing community, with support mainly for specific use cases related to the pre-built pipelines

Very large community, extensive documentation, and support

Development Ease

Easier to deploy for specific use cases due to pre-built models and components

Steeper learning curve but offers greater flexibility and control


Designed for easy integration into mobile and web applications

Can be integrated into various applications but may require more setup

  • 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.