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.

提出了一种基于师生学习框架的新方法来进行无需任何三维注释或侧面信息的三维人体姿态估计。通过姿势字典建模进行正则化以估算物理上合理的3D姿势，还提出了循环一致架构，以提高教师网络的训练效果并改进姿态估计表现。通过采用图卷积网络进行训练，进一步提高了估计准确性，同时利用知识蒸馏和促进3D旋转等变特性的循环一致架构，增强了姿态估计性能。在Human3.6M和MPI-INF-3DHP上进行了广泛的实验，相对于最先进的无监督方法将3D关节预测误差降低了11.4％，同时也优于许多使用侧面信息的弱监督方法。

不变教师和等变学生用于无监督三维人体姿态估计