MediaPipe Machine Learning Perception Pipeline

MediaPipe is a cross-platform, customizable machine learning solution framework for building perception pipelines that process real-time multimodal data including video, audio, and sensor data. Developed by Google Research (originally announced in 2019), MediaPipe provides production-ready ML solutions and a framework for building custom real-time perception applications. Key features: ready-to-use ML solutions including Face Detection ( BlazeFace, short-range and full-range models), Face Mesh (468 3D face landmarks in real-time), Hand Tracking (21 hand landmarks per hand, multi-hand tracking), Pose Estimation (33 body landmarks, full-body and upper-body modes), Holistic (combined face, hands, and body tracking), Object Detection (EfficientDet-Lite), Objectron (3D object detection for common objects like shoes, chairs, mugs), Selfie Segmentation (person segmentation for background removal), Hair Segmentation, Body Segmentation, Gesture Recognition, and Text Classification. Framework architecture: graph-based pipeline using MediaPipe Calculator Framework, where each processing step is a Calculator node, connected via Streams, enabling modular and reusable pipeline construction. Cross-platform support for Android, iOS, web (via WebAssembly and TensorFlow.js), Linux, macOS, and Windows. GPU acceleration via OpenGL ES, Metal, Vulkan, and CUDA for high-performance inference. Model Maker for custom on-device model training with transfer learning on mobile devices. Ready-to-use solutions with pre-trained models exported in TensorFlow Lite format. Integration with TensorFlow, TensorFlow Lite, and ONNX models. MediaPipe Tasks (2023) providing simplified high-level APIs for Android, iOS, Web, Python, and C++ with consistent interfaces across solutions. MediaPipe Studio web demo for testing solutions interactively. Open source under Apache-2.0.