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We propose an efficient approach to exploiting motion information fromconsecutive frames of a video sequence to recover the 3D pose of people.Previous approaches typically compute candidate poses in individual frames andthen 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 3Dpose in the central frame.  We further show that, for this approach to achieve its full potential, it isessential to compensate for the motion in consecutive frames so that thesubject remains centered. This then allows us to effectively overcomeambiguities and improve upon the state-of-the-art by a large margin on theHuman3.6m, HumanEva, and KTH Multiview Football 3D human pose estimationbenchmarks.

Direct Prediction of 3D Body Poses from Motion Compensated Sequences