构建跨数据集评估框架，揭示3D人体姿态估计的泛化短板
PoseBench3D: A Cross-Dataset Analysis Framework for 3D Human Pose Estimation via Pose Lifting Networks
- 设计统一接口自动评估18种方法在4个数据集上的表现
- 发现多数方法在新数据集上性能下降超20%（MPJPE）
- 适合关注模型泛化能力的研究者和实际部署开发者
可靠的三维人体姿态估计（3D HPE）仍面临视角、环境和相机设置差异带来的挑战。尽管许多方法在单个数据集上表现接近最优，但在未见数据集上性能显著下降。现实中系统需适应多样视角、环境和相机配置，而这些条件常与训练时不同。人工评估跨数据集性能极为耗时。为此，我们提出PoseBench3D，一个标准化测试框架，通过单一可配置界面实现对四个常用3D HPE数据集的一致公平比较。利用该框架，我们重评了18种方法，并报告超过100个跨数据集结果（协议1：MPJPE；协议2：PA-MPJPE），揭示出系统性泛化差距及常见预处理与数据集设置选择的影响。代码已开源：https://github.com/bryanjvela/PoseBench3D。
原文摘要 · Abstract (English)
Reliable three-dimensional human pose estimation (3D HPE) remains challenging due to the differences in viewpoints, environments, and camera conventions among datasets. As a result, methods that achieve near-optimal in-dataset accuracy often degrade on unseen datasets. 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. Measuring cross-dataset performance is a vital process, but extremely labor-intensive when done manually for human pose estimation. To address these challenges, we automate this evaluation using PoseBench3D, a standardized testing framework that enables consistent and fair cross-dataset comparisons on previously unseen data. PoseBench3D streamlines testing across four widely used 3D HPE datasets via a single, configurable interface. Using this framework, we re-evaluate 18 methods and report over 100 cross-dataset results under Protocol 1: MPJPE and Protocol 2: PA-MPJPE, revealing systematic generalization gaps and the impact of common preprocessing and dataset setup choices. The PoseBench3D code is found at: https://github.com/bryanjvela/PoseBench3D
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