Probablistic Restoration with Adaptive Noise Sampling for 3D Human Pose Estimation
<|reference_start|>Probablistic Restoration with Adaptive Noise Sampling for 3D Human Pose Estimation: 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.<|reference_end|>
@article{zeng2024probablistic,
    title={Probablistic Restoration with Adaptive Noise Sampling for 3D Human Pose
  Estimation},
    author={Xianzhou Zeng, Hao Qin, Ming Kong, Luyuan Chen, Qiang Zhu},
    journal={arXiv preprint arXiv:2405.02114},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02114},
    primaryClass={cs.CV}
}