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:
this https URL

基于弱监督方法，提出了适用于轻量级单假设模型的概率恢复三维人体姿势评估框架（PRPose），通过逆向映射隐藏概率分布，采用自适应噪声采样策略生成合理的多假设样本，从而解决了 2D 姿势检测误差和 2D 到 3D 非正常问题的限制，实验证明了 PRPose 的有效性和高效性。