  We propose a novel method based on teacher-student learning framework for 3D
human pose estimation without any 3D annotation or side information. To solve
this unsupervised-learning problem, the teacher network adopts
pose-dictionary-based modeling for regularization to estimate a physically
plausible 3D pose. To handle the decomposition ambiguity in the teacher
network, we propose a cycle-consistent architecture promoting a 3D
rotation-invariant property to train the teacher network. To further improve
the estimation accuracy, the student network adopts a novel graph convolution
network for flexibility to directly estimate the 3D coordinates. Another
cycle-consistent architecture promoting 3D rotation-equivariant property is
adopted to exploit geometry consistency, together with knowledge distillation
from the teacher network to improve the pose estimation performance. We conduct
extensive experiments on Human3.6M and MPI-INF-3DHP. Our method reduces the 3D
joint prediction error by 11.4% compared to state-of-the-art unsupervised
methods and also outperforms many weakly-supervised methods that use side
information on Human3.6M. Code will be available at
https://github.com/sjtuxcx/ITES.
