  The 2D human pose estimation (HPE) is a basic visual problem. However, its
supervised learning requires massive keypoint labels, which is labor-intensive
to collect. Thus, we aim at boosting a pose estimator by excavating extra
unlabeled data with semi-supervised learning (SSL). Most previous SSHPE methods
are consistency-based and strive to maintain consistent outputs for differently
augmented inputs. Under this genre, we find that SSHPE can be boosted from two
cores: advanced data augmentations and concise consistency training ways.
Specifically, for the first core, we discover the synergistic effects of
existing augmentations, and reveal novel paradigms for conveniently producing
new superior HPE-oriented augmentations which can more effectively add noise on
unlabeled samples. We can therefore establish paired easy-hard augmentations
with larger difficulty gaps. For the second core, we propose to repeatedly
augment unlabeled images with diverse hard augmentations, and generate
multi-path predictions sequentially for optimizing multi-losses in a single
network. This simple and compact design is interpretable, and easily benefits
from newly found augmentations. Comparing to SOTA approaches, our method brings
substantial improvements on public datasets. Code is in
\url{https://github.com/hnuzhy/MultiAugs}
