MoveNet is an ultra-fast and accurate model that detects 17 keypoints of a body. The model is offered on TF Hub with two variants (Lightning and Thunder). Lightning is intended for latency-critical applications, while Thunder is intended for applications that require high accuracy. Both models run faster than real time (30+ FPS) on most modern desktops, laptops, and phones, which proves crucial for live fitness, sports, and health applications. This is achieved by running the model completely client-side, in the browser using TensorFlow.js with no server calls needed after the initial page load and no dependencies to install.
Human pose estimation has come a long way in the last five years, but surprisingly has not surfaced in many applications just yet. This is because more focus has been placed on making pose models larger and more accurate, rather than doing the engineering work to make them fast and deployable everywhere. The mission is to design and optimize a model that leverages the best aspects of state-of-the-art architectures, while keeping inference times as low as possible. The result is a model that can deliver accurate keypoints across a wide variety of poses, environments, and hardware setups.
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