Uncertainty-Aware Testing-Time Optimization for 3D Human Pose Estimation
In this paper, we propose an Uncertainty-Aware testing-time Optimization (UAO) framework for 3D human pose estimation. During the training process, we propose the GUMLP to estimate 3D results and uncertainty values for each joint. For test-time optimization, our UAO framework freezes the pre-trained network parameters and optimizes a latent state initialized by the input 2D pose. To constrain the optimization direction in both 2D and 3D spaces, projection and uncertainty constraints are applied. Extensive experiments show that our approach achieves state-of-the-art performance on two popular datasets
Jun 15, 2025
HYRE: Hybrid Regressor for 3D Human Pose and Shape Estimation
A novel Hybrid Regressor (HYRE) that combines parametric and non-parametric paradigms for 3D human pose and shape estimation, bridging the gap between physically plausible and pixel-aligned results through joint learning.
Dec 25, 2024