论文标题
3D人类姿势估计和本地化的时间平滑
Temporal Smoothing for 3D Human Pose Estimation and Localization for Occluded People
论文作者
论文摘要
在多人姿势估计中，参与者可能会被严重阻塞，甚至在另一个人后面变得完全看不见。虽然时间方法仍然可以预测使用过去和将来框架暂时消失的姿势的合理估计，但它们仍然显示出很大的错误。我们提出了一种能量最小化的方法，可以在时间上产生光滑，有效的轨迹，并在可见度中弥合差距。我们表明，它比其他基于插值的方法要好，并实现了最新结果的状态。此外，我们介绍合成粘液-TEMP数据集，这是MUCO-3DHP数据集的时间扩展。我们的代码可公开可用。
In multi-person pose estimation actors can be heavily occluded, even become fully invisible behind another person. While temporal methods can still predict a reasonable estimation for a temporarily disappeared pose using past and future frames, they exhibit large errors nevertheless. We present an energy minimization approach to generate smooth, valid trajectories in time, bridging gaps in visibility. We show that it is better than other interpolation based approaches and achieves state of the art results. In addition, we present the synthetic MuCo-Temp dataset, a temporal extension of the MuCo-3DHP dataset. Our code is made publicly available.
