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 es- timation accuracy. By 2021, HyperPose has received over 1000 stars on GitHub and attracted users from both industry and academy.