Using Motion Cues to Supervise Single-Frame Body Pose and Shape
  Estimation in Low Data Regimes
Andrey Davydov Alexey Sidnev Artsiom Sanakoyeu Yuhua Chen Mathieu Salzmann Pascal Fua
When enough annotated training data is available, supervised deep-learning
algorithms excel at estimating human body pose and shape using a single camera.
The effects of too little such data being available can be mitigated by using
other information sources, such as databases of body shapes, to learn priors.
Unfortunately, such sources are not always available either. We show that, in
such cases, easy-to-obtain unannotated videos can be used instead to provide
the required supervisory signals. Given a trained model using too little
annotated data, we compute poses in consecutive frames along with the optical
flow between them. We then enforce consistency between the image optical flow
and the one that can be inferred from the change in pose from one frame to the
next. This provides enough additional supervision to effectively refine the
network weights and to perform on par with methods trained using far more
annotated data.