  We present BlazePose, a lightweight convolutional neural network architecture
for human pose estimation that is tailored for real-time inference on mobile
devices. During inference, the network produces 33 body keypoints for a single
person and runs at over 30 frames per second on a Pixel 2 phone. This makes it
particularly suited to real-time use cases like fitness tracking and sign
language recognition. Our main contributions include a novel body pose tracking
solution and a lightweight body pose estimation neural network that uses both
heatmaps and regression to keypoint coordinates.
