  Driven by recent computer vision and robotic applications, recovering 3D
human poses has become increasingly important and attracted growing interests.
In fact, completing this task is quite challenging due to the diverse
appearances, viewpoints, occlusions and inherently geometric ambiguities inside
monocular images. Most of the existing methods focus on designing some
elaborate priors /constraints to directly regress 3D human poses based on the
corresponding 2D human pose-aware features or 2D pose predictions. However, due
to the insufficient 3D pose data for training and the domain gap between 2D
space and 3D space, these methods have limited scalabilities for all practical
scenarios (e.g., outdoor scene). Attempt to address this issue, this paper
proposes a simple yet effective self-supervised correction mechanism to learn
all intrinsic structures of human poses from abundant images. Specifically, the
proposed mechanism involves two dual learning tasks, i.e., the 2D-to-3D pose
transformation and 3D-to-2D pose projection, to serve as a bridge between 3D
and 2D human poses in a type of "free" self-supervision for accurate 3D human
pose estimation. The 2D-to-3D pose implies to sequentially regress intermediate
3D poses by transforming the pose representation from the 2D domain to the 3D
domain under the sequence-dependent temporal context, while the 3D-to-2D pose
projection contributes to refining the intermediate 3D poses by maintaining
geometric consistency between the 2D projections of 3D poses and the estimated
2D poses. We further apply our self-supervised correction mechanism to develop
a 3D human pose machine, which jointly integrates the 2D spatial relationship,
temporal smoothness of predictions and 3D geometric knowledge. Extensive
evaluations demonstrate the superior performance and efficiency of our
framework over all the compared competing methods.
