An accurate and uncertainty-aware 3D human body pose estimation is key to
enabling truly safe but efficient human-robot interactions. Current
uncertainty-aware methods in 3D human pose estimation are limited to predicting
the uncertainty of the body posture, while effectively neglecting the body
shape and root pose. In this work, we present GloPro, which to the best of our
knowledge the first framework to predict an uncertainty distribution of a 3D
body mesh including its shape, pose, and root pose, by efficiently fusing
visual clues with a learned motion model. We demonstrate that it vastly
outperforms state-of-the-art methods in terms of human trajectory accuracy in a
world coordinate system (even in the presence of severe occlusions), yields
consistent uncertainty distributions, and can run in real-time. Our code will
be released upon acceptance at this https URL

本研究提出 GloPro 框架，通过有效地融合视觉线索与学习到的动作模型，首次预测包括形状、姿态和根姿态在内的三维人体网格的不确定性分布，大大超越了现有方法在人类轨迹准确度方面的表现，并能够实时运行。

GloPro: 全球一致的不确定性感知的野外三维人体姿势估计与跟踪

GloPro: Globally-Consistent Uncertainty-Aware 3D Human Pose Estimation &  Tracking in the Wild

Recently, uncertainty-aware methods have attracted increasing attention in
semi-supervised medical image segmentation. However, current methods usually
suffer from the drawback that it is difficult to balance the computational
cost, estimation accuracy, and theoretical support in a unified framework. To
alleviate this problem, we introduce the Dempster-Shafer Theory of Evidence
(DST) into semi-supervised medical image segmentation, dubbed Evidential
Inference Learning (EVIL). EVIL provides a theoretically guaranteed solution to
infer accurate uncertainty quantification in a single forward pass. Trustworthy
pseudo labels on unlabeled data are generated after uncertainty estimation. The
recently proposed consistency regularization-based training paradigm is adopted
in our framework, which enforces the consistency on the perturbed predictions
to enhance the generalization with few labeled data. Experimental results show
that EVIL achieves competitive performance in comparison with several
state-of-the-art methods on the public dataset.

通过将证据理论引入半监督医学图像分割，以在单次前向传递中推理准确的不确定性量化为目的，提出了命名为 EVIL 的确定性推理学习方法。该方法使用一致性正则化训练范式，通过对扰动预测的一致性进行约束来增强少量标记数据的泛化能力。实验结果表明，EVIL 在公共数据集上与几种最先进的方法相比取得了竞争性的性能。