Camera distance helps 3D hand pose estimated from a single RGB image.

Most existing methods for RGB hand pose estimation use root-relative 3D coordinates for supervision. However, such supervision neglects the distance between the camera and the object (i.e., the hand). The camera distance is especially important under a perspective camera, which controls the depth-dependent scaling of the perspective projection. As a result, the same hand pose, with different camera distances can be projected into different 2D shapes by the same perspective camera. Neglecting such important information results in ambiguities in recovering 3D poses from 2D images. In this article, we propose a camera projection learning module (CPLM) that uses the scale factor contained in the camera distance to associate 3D hand pose with 2D UV coordinates, which facilities to further optimize the accuracy of the estimated hand joints. Specifically, following the previous work, we use a two-stage RGB-to-2D and 2D-to-3D method to estimate 3D hand pose and embed a graph convolutional network in the second stage to leverage the information contained in the complex non-Euclidean structure of 2D hand joints. Experimental results demonstrate that our proposed method surpasses state-of-the-art methods on the benchmark dataset RHD and obtains competitive results on the STB and D+O datasets. [Display omitted] • The same hand pose projected different 2D shapes due to the camera distance. • Camera Projection Learning Module optimized the accuracy of the estimated hand pose. • The proposed two-stage network obtained good results. [ABSTRACT FROM AUTHOR]