  Although monocular 3D human pose estimation methods have made significant
progress, it is far from being solved due to the inherent depth ambiguity.
Instead, exploiting multi-view information is a practical way to achieve
absolute 3D human pose estimation. In this paper, we propose a simple yet
effective pipeline for weakly-supervised cross-view 3D human pose estimation.
By only using two camera views, our method can achieve state-of-the-art
performance in a weakly-supervised manner, requiring no 3D ground truth but
only 2D annotations. Specifically, our method contains two steps: triangulation
and refinement. First, given the 2D keypoints that can be obtained through any
classic 2D detection methods, triangulation is performed across two views to
lift the 2D keypoints into coarse 3D poses. Then, a novel cross-view U-shaped
graph convolutional network (CV-UGCN), which can explore the spatial
configurations and cross-view correlations, is designed to refine the coarse 3D
poses. In particular, the refinement progress is achieved through
weakly-supervised learning, in which geometric and structure-aware consistency
checks are performed. We evaluate our method on the standard benchmark dataset,
Human3.6M. The Mean Per Joint Position Error on the benchmark dataset is 27.4
mm, which outperforms existing state-of-the-art methods remarkably (27.4 mm vs
30.2 mm).
