  Estimating human pose is an important yet challenging task in multimedia
applications. Existing pose estimation libraries target reproducing standard
pose estimation algorithms. When it comes to customising these algorithms for
real-world applications, none of the existing libraries can offer both the
flexibility of developing custom pose estimation algorithms and the
high-performance of executing these algorithms on commodity devices. In this
paper, we introduce Hyperpose, a novel flexible and high-performance pose
estimation library. Hyperpose provides expressive Python APIs that enable
developers to easily customise pose estimation algorithms for their
applications. It further provides a model inference engine highly optimised for
real-time pose estimation. This engine can dynamically dispatch carefully
designed pose estimation tasks to CPUs and GPUs, thus automatically achieving
high utilisation of hardware resources irrespective of deployment environments.
Extensive evaluation results show that Hyperpose can achieve up to 3.1x~7.3x
higher pose estimation throughput compared to state-of-the-art pose estimation
libraries without compromising estimation accuracy. By 2021, Hyperpose has
received over 1000 stars on GitHub and attracted users from both industry and
academy.
