TensorFlow recently launched its latest pose detection model, MoveNet, with a new pose-detection API in TensorFlow.js. MoveNet is a very fast and accurate model that detects 17 keypoints of a body. The model is offered with two variants, called Lightning and Thunder. Both the models run faster than real time (i.e.,30 FramesPerSecond) on most modern desktops, laptops, and phones. The models run completely on client-side, in the browser using TensorFlow.js Human pose estimation has developed a lot; however, it hasn't surfaced in many applications, mainly because more focus has been placed on making pose models larger and more accurate than making them faster and easily deployable everywhere.

Ahead of Google I/O, Google Research launched a new pose detection model in TensorFlow.js called MoveNet. This ultra-fast and accurate model can detect 17 key points in the human body. MoveNet is currently available on TF Hub with two variants -- Lightning and Thunder. While Lightning is intended for latency-critical applications, Thunder is for applications that call for higher accuracy. Both models claim to run faster than real-time (30 frames per second (FPS)) on most personal computers, laptops and phones.