  Existing self-supervised 3D human pose estimation schemes have largely relied
on weak supervisions like consistency loss to guide the learning, which,
inevitably, leads to inferior results in real-world scenarios with unseen
poses. In this paper, we propose a novel self-supervised approach that allows
us to explicitly generate 2D-3D pose pairs for augmenting supervision, through
a self-enhancing dual-loop learning framework. This is made possible via
introducing a reinforcement-learning-based imitator, which is learned jointly
with a pose estimator alongside a pose hallucinator; the three components form
two loops during the training process, complementing and strengthening one
another. Specifically, the pose estimator transforms an input 2D pose sequence
to a low-fidelity 3D output, which is then enhanced by the imitator that
enforces physical constraints. The refined 3D poses are subsequently fed to the
hallucinator for producing even more diverse data, which are, in turn,
strengthened by the imitator and further utilized to train the pose estimator.
Such a co-evolution scheme, in practice, enables training a pose estimator on
self-generated motion data without relying on any given 3D data. Extensive
experiments across various benchmarks demonstrate that our approach yields
encouraging results significantly outperforming the state of the art and, in
some cases, even on par with results of fully-supervised methods. Notably, it
achieves 89.1% 3D PCK on MPI-INF-3DHP under self-supervised cross-dataset
evaluation setup, improving upon the previous best self-supervised methods by
8.6%. Code can be found at: https://github.com/Garfield-kh/PoseTriplet
