MediaPipe is a framework for building multimodal (eg. video, audio, any time series data) applied ML pipelines. With MediaPipe, a perception pipeline can be built as a graph of modular components, including, for instance, inference models (e.g., TensorFlow, TFLite) and media processing functions.
"MediaPipe has made it extremely easy to build our 3D person pose reconstruction demo app, facilitating accelerated neural network inference on device and synchronization of our result visualization with the video capture stream. Highly recommended!" - George Papandreou, CTO, Ariel AI
Follow these instructions.
See mobile and desktop examples.
MediaPipe Read-the-Docs or docs.mediapipe.dev
Check out the Examples page for tutorials on how to use MediaPipe. Concepts page for basic definitions
A web-based visualizer is hosted on viz.mediapipe.dev. Please also see instructions here.
- Discuss - General community discussion around MediaPipe
Open sourced at CVPR 2019 on June 17~20 in Long Beach, CA
MediaPipe is currently in alpha for v0.6. We are still making breaking API changes and expect to get to stable API by v1.0.
We welcome contributions. Please follow these guidelines.
We use GitHub issues for tracking requests and bugs. Please post questions to the MediaPipe Stack Overflow with a 'mediapipe' tag.