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

arXiv: https://arxiv.org/abs/2310.09066 · HTML: https://mlsanity.com/paper/2310.09066/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 13 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition](https://mlsanity.com/paper/2104.10166/) (2021)

## Built on by (most cited first, 3 of 3)

- [Meaningful Pose-Based Sign Language Evaluation](https://mlsanity.com/paper/2510.07453/) (2025)
- [Pose-Based Sign Language Appearance Transfer](https://mlsanity.com/paper/2410.13675/) (2024)
- [Isolated Sign Language Recognition for Icelandic Sign Language: Experiments in a Low-resource Setting](https://mlsanity.com/paper/2609.25862/) (2026)

## Abstract (arXiv)

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 textttpose-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. textttpose-format emerges as a one-stop solution, streamlining the complexities of pose data management and analysis.