Mediapipe

* [MediaPipe Iris](https://solutions.mediapipe.dev/iris) for iris tracking and single-image depth-from-iris
* Fixed mirrored text rendering in iOS example apps
* [Google AI blog post on MediaPipe Iris](https://mediapipe.page.link/iris-blog)
* [Iris tracking web demo](https://viz.mediapipe.dev/demo/iris_tracking)
* [Depth estimation from Iris web demo](https://viz.mediapipe.dev/demo/iris_depth)

* Added support for automatic provisioning for [building iOS examples](https://google.github.io/mediapipe/getting_started/building_examples.html#ios)
* Fixed sample trace file (https://github.com/google/mediapipe/issues/849)

* [TfLiteTensorsToLandmarksCalculator](https://github.com/google/mediapipe/blob/master/mediapipe/calculators/tflite/tflite_tensors_to_landmarks_calculator.cc)
* It now always [normalizes Z coordinates by image width (as for X)](https://github.com/google/mediapipe/blob/e9fbe868e55fa23aaabc31f9f847c22287062850/mediapipe/calculators/tflite/tflite_tensors_to_landmarks_calculator.cc#L223) when producing NORM_LANDMARKS output, assuming a weak perspective projection camera model.
* The [normalize_z](https://github.com/google/mediapipe/blob/e9fbe868e55fa23aaabc31f9f847c22287062850/mediapipe/calculators/tflite/tflite_tensors_to_landmarks_calculator.proto#L53) option can be used to further normalize Z coordinates by an additional factor. For instance, [normalize_z: 0.4](https://github.com/google/mediapipe/blob/e9fbe868e55fa23aaabc31f9f847c22287062850/mediapipe/graphs/hand_tracking/subgraphs/hand_landmark_gpu.pbtxt#L149) is now used in hand tracking to better account for the Z coordinate distribution in the training data.
* Bug fixes
* Fixes issues on Ubuntu 20.04, resolving 820.
* Documentation update
* Added [instructions](https://google.github.io/mediapipe/getting_started/install.html#installing-on-debian-and-ubuntu) for building for Nvidia Jetson and Raspberry Pi devices with ARM Ubuntu.

* MediaPipe Hand
* [Updated model, documentation and pipeline](https://solutions.mediapipe.dev/hands)
* Improved hand tracking quality in various scenarios
* Added handedness prediction, e.g. left or right hand.
* [See tweet for visualization](https://twitter.com/GoogleAI/status/1265319835283537921)
* MediaPipe Face Detection
* [Updated model for front-facing camera](https://solutions.mediapipe.dev/face_detection#resources)
* [Added model for back-facing camera](https://solutions.mediapipe.dev/face_detection#resources), which better handles smaller faces
* MediaPipe AutoFlip
* [Updated to use face detection model for back-facing camera](https://solutions.mediapipe.dev/autoflip)
* [Support for running MediaPipe on Windows - experimental](https://google.github.io/mediapipe/getting_started/install.html#installing-on-windows)
* MediaPipe TFLite CPU desktop live and offline demos only. No TFLite GPU and TensorFlow support yet.
* Compiled by MSVC through Bazel. No MinGW and Cygwin support yet.
* MediaPipe Android apps are NOT buildable on native Windows yet.
* [Tracing and profiling MediaPipe graphs - experimental](https://google.github.io/mediapipe/getting_started/install.html#installing-on-windows)
* [Support for collecting trace logs on Android, iOS, Linux](https://google.github.io/mediapipe/tools/tracing_and_profiling.html#collecting-the-logs)
* To analyze the trace logs, [MediaPipe Visualizer](https://viz.mediapipe.dev)
* Community related
* [Awesome MediaPipe: curation of code related to MediaPipe](https://mediapipe.org)
* [Slack community for MediaPipe users](https://mediapipe.page.link/joinslack)

* [MediaPipe KNIFT](http://solutions.mediapipe.dev/knift)
* See also [Google Developers Blog post](https://developers.googleblog.com/2020/04/mediapipe-knift-template-based-feature-matching.html)

* [MediaPipe Face Mesh](https://solutions.mediapipe.dev/face_mesh) (3D face landmark estimation with 468 landmarks)
* [MediaPipe Modules](https://github.com/google/mediapipe/tree/master/mediapipe/modules)
* [Face Detection](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection)
* [Face Landmark example](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark)