The accuracy and robustness of 3D human pose estimation (HPE) are limited by 2D pose detection errors and 2D to 3D ill-posed challenges, which have drawn great attention to Multi-Hypothesis HPE research. Most existing MH-HPE methods are based on generative models, which are computationally expensive and difficult to train. In this study, we propose a Probabilistic Restoration 3D Human Pose Estimation framework (PRPose) that can be integrated with any lightweight single-hypothesis model. Specifically, PRPose employs a weakly supervised approach to fit the hidden probability distribution of the 2D-to-3D lifting process in the Single-Hypothesis HPE model and then reverse-map the distribution to the 2D pose input through an adaptive noise sampling strategy to generate reasonable multi-hypothesis samples effectively. Extensive experiments on 3D HPE benchmarks (Human3.6M and MPI-INF-3DHP) highlight the effectiveness and efficiency of PRPose. Code is available at: https://github.com/xzhouzeng/PRPose.

翻译：三维人体姿态估计（HPE）的精度与鲁棒性受限于二维姿态检测误差及二维到三维映射的病态问题，这促使多假设HPE研究受到广泛关注。现有大多数多假设HPE方法基于生成式模型，计算成本高且训练困难。本研究提出一种概率修复三维人体姿态估计框架（PRPose），该框架可集成于任意轻量级单假设模型。具体而言，PRPose采用弱监督方法拟合单假设HPE模型中二维到三维提升过程的隐概率分布，并通过自适应噪声采样策略将该分布反向映射至二维姿态输入，从而高效生成合理的多假设样本。在3D HPE基准数据集（Human3.6M和MPI-INF-3DHP）上的大量实验验证了PRPose的有效性和高效性。代码开源地址：https://github.com/xzhouzeng/PRPose。