Probablistic Restoration with Adaptive Noise Sampling for 3D Human Pose Estimation
CoRR(2024)
摘要
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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