  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.
