  We propose a new semi-supervised learning design for human pose estimation
that revisits the popular dual-student framework and enhances it two ways.
First, we introduce a denoising scheme to generate reliable pseudo-heatmaps as
targets for learning from unlabeled data. This uses multi-view augmentations
and a threshold-and-refine procedure to produce a pool of pseudo-heatmaps.
Second, we select the learning targets from these pseudo-heatmaps guided by the
estimated cross-student uncertainty. We evaluate our proposed method on
multiple evaluation setups on the COCO benchmark. Our results show that our
model outperforms previous state-of-the-art semi-supervised pose estimators,
especially in extreme low-data regime. For example with only 0.5K labeled
images our method is capable of surpassing the best competitor by 7.22 mAP
(+25% absolute improvement). We also demonstrate that our model can learn
effectively from unlabeled data in the wild to further boost its generalization
and performance.
