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

We propose a CNN-based approach for 3D human body pose estimation from single
RGB images that addresses the issue of limited generalizability of models
trained solely on the starkly limited publicly available 3D pose data. Using
only the existing 3D pose data and 2D pose data, we show state-of-the-art
performance on established benchmarks through transfer of learned features,
while also generalizing to in-the-wild scenes. We further introduce a new
training set for human body pose estimation from monocular images of real
humans that has the ground truth captured with a multi-camera marker-less
motion capture system. It complements existing corpora with greater diversity
in pose, human appearance, clothing, occlusion, and viewpoints, and enables an
increased scope of augmentation. We also contribute a new benchmark that covers
outdoor and indoor scenes, and demonstrate that our 3D pose dataset shows
better in-the-wild performance than existing annotated data, which is further
improved in conjunction with transfer learning from 2D pose data. All in all,
we argue that the use of transfer learning of representations in tandem with
algorithmic and data contributions is crucial for general 3D body pose
estimation.

提出了一种基于 CNN 的方法，利用现有的 3D 姿势数据和 2D 姿势数据进行迁移学习，在真实场景中实现了最先进的性能，同时引入了一个人类身体姿势估计的新训练集，并提出了一个覆盖室内和室外场景的新基准。