Move Mirror: An AI Experiment with Pose Estimation in the Browser using TensorFlow.js

Pose estimation, or the ability to detect humans and their poses from image data, is one of the most exciting -- and most difficult -- topics in machine learning and computer vision. Recently, Google shared PoseNet: a state-of-the-art pose estimation model that provides highly accurate pose data from image data (even when those images are blurry, low-resolution, or in black and white). This is the story of the experiment that prompted us to create this pose estimation library for the web in the first place. Months ago, we prototyped a fun experiment called Move Mirror that lets you explore images in your browser, just by moving around. The experiment creates a unique, flipbook-like experience that follows your moves and reflects them with images of all kinds of human movement -- from sports and dance to martial arts, acting, and beyond.