Existing 3D human pose estimation methods perform remarkably well in both
monocular and multi-view settings. However, their efficacy diminishes
significantly in the presence of heavy occlusions, which limits their practical
utility. For video sequences, temporal continuity can help infer accurate
poses, especially in heavily occluded frames. In this paper, we aim to leverage
this potential of temporal continuity through human motion priors, coupled with
large-scale pre-training on 3D poses and self-supervised learning, to enhance
3D pose estimation in a given video sequence. This leads to a temporally
continuous 3D pose estimate on unlabelled in-the-wild videos, which may contain
occlusions, while exclusively relying on pre-trained 3D pose models. We propose
an unsupervised method named TEMP3D that aligns a motion prior model on a given
in-the-wild video using existing SOTA single image-based 3D pose estimation
methods to give temporally continuous output under occlusions. To evaluate our
method, we test it on the Occluded Human3.6M dataset, our custom-built dataset
which contains significantly large (up to 100%) human body occlusions
incorporated into the Human3.6M dataset. We achieve SOTA results on Occluded
Human3.6M and the OcMotion dataset while maintaining competitive performance on
non-occluded data. URL: this https URL

在具有重度遮挡的视频序列中，通过使用人体运动先验、大规模预训练的三维姿势和自监督学习，借助于时间连续性的潜力，提高给定视频序列中的三维姿势估计，从而在包含遮挡的自然环境视频中得到连续的三维姿势估计。