  Managing and analyzing pose data is a complex task, with challenges ranging
from handling diverse file structures and data types to facilitating effective
data manipulations such as normalization and augmentation. This paper presents
\texttt{pose-format}, a comprehensive toolkit designed to address these
challenges by providing a unified, flexible, and easy-to-use interface. The
library includes a specialized file format that encapsulates various types of
pose data, accommodating multiple individuals and an indefinite number of time
frames, thus proving its utility for both image and video data. Furthermore, it
offers seamless integration with popular numerical libraries such as NumPy,
PyTorch, and TensorFlow, thereby enabling robust machine-learning applications.
Through benchmarking, we demonstrate that our \texttt{.pose} file format offers
vastly superior performance against prevalent formats like OpenPose, with added
advantages like self-contained pose specification. Additionally, the library
includes features for data normalization, augmentation, and easy-to-use
visualization capabilities, both in Python and Browser environments.
\texttt{pose-format} emerges as a one-stop solution, streamlining the
complexities of pose data management and analysis.
