  Recently, the vision transformer and its variants have played an increasingly
important role in both monocular and multi-view human pose estimation.
Considering image patches as tokens, transformers can model the global
dependencies within the entire image or across images from other views.
However, global attention is computationally expensive. As a consequence, it is
difficult to scale up these transformer-based methods to high-resolution
features and many views.
  In this paper, we propose the token-Pruned Pose Transformer (PPT) for 2D
human pose estimation, which can locate a rough human mask and performs
self-attention only within selected tokens. Furthermore, we extend our PPT to
multi-view human pose estimation. Built upon PPT, we propose a new cross-view
fusion strategy, called human area fusion, which considers all human foreground
pixels as corresponding candidates. Experimental results on COCO and MPII
demonstrate that our PPT can match the accuracy of previous pose transformer
methods while reducing the computation. Moreover, experiments on Human 3.6M and
Ski-Pose demonstrate that our Multi-view PPT can efficiently fuse cues from
multiple views and achieve new state-of-the-art results.
