PoseBench3D: A Cross-Dataset Analysis Framework for 3D Human Pose Estimation

• URL: http://arxiv.org/abs/2505.10888v1
• Date: Fri, 16 May 2025 05:49:23 GMT
• Title: PoseBench3D: A Cross-Dataset Analysis Framework for 3D Human Pose Estimation
• Authors: Saad Manzur, Bryan Vela, Brandon Vela, Aditya Agrawal, Lan-Anh Dang-Vu, David Li, Wayne Hayes,
• Abstract summary: We present a standardized testing environment in which each method is evaluated on a variety of datasets.<n>We propose PoseBench3D, a unified framework designed to systematically re-evaluate prior and future models.
• Score: 1.470703050699957
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable three-dimensional human pose estimation is becoming increasingly important for real-world applications, yet much of prior work has focused solely on the performance within a single dataset. In practice, however, systems must adapt to diverse viewpoints, environments, and camera setups -- conditions that differ significantly from those encountered during training, which is often the case in real-world scenarios. To address these challenges, we present a standardized testing environment in which each method is evaluated on a variety of datasets, ensuring consistent and fair cross-dataset comparisons -- allowing for the analysis of methods on previously unseen data. Therefore, we propose PoseBench3D, a unified framework designed to systematically re-evaluate prior and future models across four of the most widely used datasets for human pose estimation -- with the framework able to support novel and future datasets as the field progresses. Through a unified interface, our framework provides datasets in a pre-configured yet easily modifiable format, ensuring compatibility with diverse model architectures. We re-evaluated the work of 18 methods, either trained or gathered from existing literature, and reported results using both Mean Per Joint Position Error (MPJPE) and Procrustes Aligned Mean Per Joint Position Error (PA-MPJPE) metrics, yielding more than 100 novel cross-dataset evaluation results. Additionally, we analyze performance differences resulting from various pre-processing techniques and dataset preparation parameters -- offering further insight into model generalization capabilities.

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