Abstract
This paper tackles the problem of predicting 3D Human Pose on AI Edge Devices. An AI edge device is an AI-enabled Internet of Things (IoT) device that can run Deep Learning (DL) models right on the device without connecting to the Internet. In recent years, researchers have proposed numerous DL-based models for 3D human pose estimation (HPE), but no work focuses on solving this task in edge devices. Building a DL model for an edge device has unique challenges such as limited computation capacity, small memory, and low power. This paper investigates how to optimize a big, heavy-computing 3D pose estimation DL model into a light-weight, small-size model that can run efficiently in a device. Specially, we propose an End-to-End pipeline to run 3D human pose prediction (HPP) in Real-time on AI edge devices. Furthermore, our proposed end-to-end pipeline is general and can be employed by other AI edge device based real-world applications.
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