MediaPipe Solutions provides a suite of libraries and tools for you to quickly apply artificial intelligence (AI)

A Streamlit GUI for exploring the functionality of some of Google's Mediapipe Machine Learning solutions, viz: Hand Tracking; Pose Estimation; Face Detection; and Face Mesh.

This project integrates MediaPipe Solutions with Node.js and Express for real-time computer vision tasks. It showcases examples of image segmentation, hand and face detection, and pose detection, with a combined example for all three types of landmark detection.

AI Face Body and Hand Pose Detection with Opencv-Python and Mediapipe

mediapipe uses in face mesh3d, body and hand pose, object detection

A collection of advanced computer vision projects using OpenCV and MediaPipe for real-time Face Detection, Face Mesh, Hand Tracking, Pose Estimation, and Image Processing techniques.

People with hearing impairments communicate with hand gestures, therefore normal people encounter problems recognising their language. Hence, there is a need for systems that recognize the different signs and convey the information to the general public. Our Sign Language Detector uses a Holistic tracking feature from MediaPipe framework and Computer Vision that enables simultaneous detection of body, hand pose and face landmarks on mobile devices. Our project performs action detection that uses a sequence of data rather than a single frame for detection.

Face, Hand, and Pose Detection Using Mediapipe and OpenCV

In this Project Holistic Detection, it detect Left and Right Hand, Face Mesh, and Pose Detection using Libraries Opencv and Mediapipe in Python.

Modular computer vision project using OpenCV and MediaPipe. Includes hand tracking, pose estimation, and face mesh detection modules, plus a universal wrapper to run them individually or together. Supports webcam and video input for real-time CV experimentation.

This repo allows you to use Mediapipe's Holistic(Pose Landmark, Hand Landmark, and Face Landmark Detection) for ros2

Real-time face, pose, and hand detection with OpenCV, MediaPipe, and DeepFace for age, gender, emotion, race, and celebrity look-alike analysis

Face Detection • Emotion Recognition • Age & Gender Prediction • Face Recognition • Hands Tracking • Pose Estimation • Heart-Rate (rPPG) Built using OpenCV, MediaPipe, optional TensorFlow models, SciPy, and more.

This repository contains several projects built with the MediaPipe library. Each project shows how to use MediaPipe for tasks like face detection, hand tracking, and pose estimation with easy, beginner-friendly code.

mpkit-cc - A Python wrapper for MediaPipe that simplifies real-time hand, face, and pose detection. Just pip install mpkit-cc to add computer vision superpowers to your projects with minimal code. Perfect for gesture control, AR apps, and motion tracking. 🔥

Face, Hands and Pose Detection using the MediaPipe library

Implementation of MediaPipe ML Solutions' face, pose and hand detection

Hand Tracking, Pose Estimation, Face Detection and Face Mesh modules with mediapipe

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AI Face Body and Hand Pose Detection using Python and Mediapipe

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AI Face Body and Hand Pose Detection with Python and Mediapipe

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Mediapipe is a cross-platform library developed by Google that provides amazing ready-to-use ML solutions for computer vision tasks. OpenCV library in python is a computer vision library that is widely used for image analysis, image processing, detection, recognition, etc.

A repository based on Google's MediaPipe framework for Face, Hand and Pose Detection.

A collection of computer vision projects using MediaPipe and Python for real-time hand tracking, face detection, and pose detection.

Real-time computer vision suite using MediaPipe and OpenCV — face detection, hand tracking, pose estimation, and gesture recognition

Sample applications of mediapipe framework showcasing its different use cases like pose estimation, face detection, hand tracking etc.

Real-time AI system for face, hand, and pose detection using MediaPipe and OpenCV. Designed for interactive and creative computer vision applications.

A collection of small and practical computer vision projects built using OpenCV, MediaPipe, dlib, and face_recognition. Includes face detection, face recognition, hand tracking, pose estimation, and other core CV tasks.