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.

本文提出了一种高效利用视频序列中连续帧运动信息来恢复人的三维姿态的方法，并通过回归边界框的时空体到中心帧的 3D 姿势来改进现有方法。同时，为了最大限度地发挥这种方法的潜力，本文阐明必须补偿连续帧中的运动，以使被测量者保持中心，从而能够有效地消除歧义并在 Human3.6m、HumanEva 和 KTH Multiview Football 3D 人体姿态估计基准测试中取得显著改进。