Probablistic Restoration with Adaptive Noise Sampling for 3D Human Pose Estimation

• URL: http://arxiv.org/abs/2405.02114v1
• Date: Fri, 3 May 2024 14:14:27 GMT
• Title: Probablistic Restoration with Adaptive Noise Sampling for 3D Human Pose Estimation
• Authors: Xianzhou Zeng, Hao Qin, Ming Kong, Luyuan Chen, Qiang Zhu,
• Abstract summary: The accuracy and robustness of 3D human pose estimation (HPE) are limited by 2D pose detection errors and 2D to 3D ill-posed challenges. We propose a Probabilistic Restoration 3D Human Pose Estimation framework (PRPose) that can be integrated with any lightweight single-hypothesis model.
• Score: 2.855838363978158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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