The "lifting from 2D pose" method has been the dominant approach to 3D Human
Pose Estimation (3DHPE) due to the powerful visual analysis ability of 2D pose
estimators. Widely known, there exists a depth ambiguity problem when
estimating solely from 2D pose, where one 2D pose can be mapped to multiple 3D
poses. Intuitively, the rich semantic and texture information in images can
contribute to a more accurate "lifting" procedure. Yet, existing research
encounters two primary challenges. Firstly, the distribution of image data in
3D motion capture datasets is too narrow because of the laboratorial
environment, which leads to poor generalization ability of methods trained with
image information. Secondly, effective strategies for leveraging image
information are lacking. In this paper, we give new insight into the cause of
poor generalization problems and the effectiveness of image features. Based on
that, we propose an advanced framework. Specifically, the framework consists of
two stages. First, we enable the keypoints to query and select the beneficial
features from all image patches. To reduce the keypoints attention to
inconsequential background features, we design a novel Pose-guided Transformer
Layer, which adaptively limits the updates to unimportant image patches. Then,
through a designed Adaptive Feature Selection Module, we prune less significant
image patches from the feature map. In the second stage, we allow the keypoints
to further emphasize the retained critical image features. This progressive
learning approach prevents further training on insignificant image features.
Experimental results show that our model achieves state-of-the-art performance
on both the Human3.6M dataset and the MPI-INF-3DHP dataset.

深度学习技术用于估计人体姿势时，由于仅凭借 2D 姿势估计存在深度模糊问题，这篇论文提出了一种新的框架，通过利用图像特征和逐步学习的方法解决了泛化能力差的问题，并在多个数据集上取得了最先进的性能。

基于图像的抬举 —— 利用图像线索进行精确的三维人体姿势估计

Lifting by Image -- Leveraging Image Cues for Accurate 3D Human Pose  Estimation

Following the success of deep convolutional networks, state-of-the-art
methods for 3d human pose estimation have focused on deep end-to-end systems
that predict 3d joint locations given raw image pixels. Despite their excellent
performance, it is often not easy to understand whether their remaining error
stems from a limited 2d pose (visual) understanding, or from a failure to map
2d poses into 3-dimensional positions. With the goal of understanding these
sources of error, we set out to build a system that given 2d joint locations
predicts 3d positions. Much to our surprise, we have found that, with current
technology, "lifting" ground truth 2d joint locations to 3d space is a task
that can be solved with a remarkably low error rate: a relatively simple deep
feed-forward network outperforms the best reported result by about 30\% on
Human3.6M, the largest publicly available 3d pose estimation benchmark.
Furthermore, training our system on the output of an off-the-shelf
state-of-the-art 2d detector (\ie, using images as input) yields state of the
art results -- this includes an array of systems that have been trained
end-to-end specifically for this task. Our results indicate that a large
portion of the error of modern deep 3d pose estimation systems stems from their
visual analysis, and suggests directions to further advance the state of the
art in 3d human pose estimation.

通过构建一个借助于当前技术能在控制误差较低前提下从 2D 开环位置中提取 3D 位置任务的相对简单的深度前向网络，我们发现了现代深度 3D 姿态估计系统的可视分析引起的错误是其主要问题。