mediapipe

Cross-platform, customizable ML solutions for live and streaming media.

A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.

Web Augmented Reality. Image Tracking, Face Tracking. Tensorflow.js

GPU Accelerated MediaPipe Plugin for TouchDesigner

Unity plugin to run MediaPipe

JS Expert Week 7.0 - 🙅🤏🏻 Controlling Streaming Platforms using Eye and Hand Detection 👁🖐

A full-body keyboard using gestures to type through computer vision

XR Animator, AI-based Full Body Motion Capture and Extended Reality (XR) solution, powered by System Animator Online

Faking your webcam background under GNU/Linux, now supports background blurring, animated background, colour map effect, hologram effect and on-demand processing.

An awesome list of TensorFlow Lite models, samples, tutorials, tools and learning resources.

This is a Computer vision package that makes its easy to run Image processing and AI functions. At the core it uses OpenCV and Mediapipe libraries.

realtime motion tracking in blender using mediapipe and rigify

Windows desktop app that turns long-form YouTube videos into 9:16 short-form clips — AI highlight detection, face-tracking portrait reframe, and word-by-word captions.

TensorFlow Lite Samples on Unity

Virtually controlling computer using hand-gestures and voice commands. Using MediaPipe, OpenCV Python.

This is a sample program that recognizes hand signs and finger gestures with a simple MLP using the detected key points. Handpose is estimated using MediaPipe.

Move things on your screen with your bare hands. A webcam-powered, hand-tracked interface for your AI. No headset. No controllers.

MediaPipe(Python版)を用いて手の姿勢推定を行い、検出したキーポイントを用いて、簡易なMLPでハンドサインとフィンガージェスチャーを認識するサンプルプログラムです。（Estimate hand pose using MediaPipe(Python version). This is a sample program that recognizes hand signs and finger gestures with a simple MLP using the detected key points.）

Virtual Video Device for Background Replacement with Deep Semantic Segmentation

Real-time portrait segmentation for mobile devices