pose-format: Library for Viewing, Augmenting, and Handling .pose Files

TL;DR

pose-format is a versatile library that simplifies managing, augmenting, and analyzing pose data across images and videos, supporting multiple formats and integrating with machine learning frameworks.

Contribution

It introduces a unified, efficient .pose file format and a comprehensive toolkit for pose data handling, normalization, augmentation, and visualization.

Findings

Superior performance over OpenPose formats

Supports multiple individuals and time frames

Seamless integration with NumPy, PyTorch, TensorFlow

Abstract

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.