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The "lifting from 2D pose" method has been the dominant approach to 3D HumanPose Estimation (3DHPE) due to the powerful visual analysis ability of 2D poseestimators. Widely known, there exists a depth ambiguity problem whenestimating solely from 2D pose, where one 2D pose can be mapped to multiple 3Dposes. Intuitively, the rich semantic and texture information in images cancontribute to a more accurate "lifting" procedure. Yet, existing researchencounters two primary challenges. Firstly, the distribution of image data in3D motion capture datasets is too narrow because of the laboratorialenvironment, which leads to poor generalization ability of methods trained withimage information. Secondly, effective strategies for leveraging imageinformation are lacking. In this paper, we give new insight into the cause ofpoor generalization problems and the effectiveness of image features. Based onthat, we propose an advanced framework. Specifically, the framework consists oftwo stages. First, we enable the keypoints to query and select the beneficialfeatures from all image patches. To reduce the keypoints attention toinconsequential background features, we design a novel Pose-guided TransformerLayer, which adaptively limits the updates to unimportant image patches. Then,through a designed Adaptive Feature Selection Module, we prune less significantimage patches from the feature map. In the second stage, we allow the keypointsto further emphasize the retained critical image features. This progressivelearning approach prevents further training on insignificant image features.Experimental results show that our model achieves state-of-the-art performanceon both the Human3.6M dataset and the MPI-INF-3DHP dataset.

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