Occlusion Resilient 3D Human Pose Estimation
Soumava Kumar Roy Ilia Badanin Sina Honari Pascal Fua
Occlusions remain one of the key challenges in 3D body pose estimation from
single-camera video sequences. Temporal consistency has been extensively used
to mitigate their impact but the existing algorithms in the literature do not
explicitly model them.
  Here, we apply this by representing the deforming body as a spatio-temporal
graph. We then introduce a refinement network that performs graph convolutions
over this graph to output 3D poses. To ensure robustness to occlusions, we
train this network with a set of binary masks that we use to disable some of
the edges as in drop-out techniques.
  In effect, we simulate the fact that some joints can be hidden for periods of
time and train the network to be immune to that. We demonstrate the
effectiveness of this approach compared to state-of-the-art techniques that
infer poses from single-camera sequences.