  We propose an efficient approach to exploiting motion information from
consecutive frames of a video sequence to recover the 3D pose of people.
Previous approaches typically compute candidate poses in individual frames and
then link them in a post-processing step to resolve ambiguities. By contrast,
we directly regress from a spatio-temporal volume of bounding boxes to a 3D
pose in the central frame.
  We further show that, for this approach to achieve its full potential, it is
essential to compensate for the motion in consecutive frames so that the
subject remains centered. This then allows us to effectively overcome
ambiguities and improve upon the state-of-the-art by a large margin on the
Human3.6m, HumanEva, and KTH Multiview Football 3D human pose estimation
benchmarks.
