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.

翻译：管理和分析姿态数据是一项复杂的任务，其挑战包括处理多样化的文件结构和数据类型，以及促进诸如归一化和增强等有效的数据操作。本文介绍了 pose-format，这是一个全面的工具包，旨在通过提供统一、灵活且易于使用的接口来应对这些挑战。该库包含一种专门的文件格式，可封装多种类型的姿态数据，支持多个个体和无限的时间帧，从而证明其在图像和视频数据方面的实用性。此外，它与 NumPy、PyTorch 和 TensorFlow 等流行的数值库无缝集成，从而支持强大的机器学习应用。通过基准测试，我们证明了我们的 .pose 文件格式在性能上远优于 OpenPose 等常见格式，并具有自包含姿态规范等额外优势。此外，该库还包含数据归一化、增强以及在 Python 和浏览器环境中易于使用的可视化功能。pose-format 成为一种一站式解决方案，简化了姿态数据管理和分析的复杂性。