Understanding the camera wearer's activity is central to egocentric vision,
yet one key facet of that activity is inherently invisible to the camera--the
wearer's body pose. Prior work focuses on estimating the pose of hands and arms
when they come into view, but this 1) gives an incomplete view of the full body
posture, and 2) prevents any pose estimate at all in many frames, since the
hands are only visible in a fraction of daily life activities. We propose to
infer the "invisible pose" of a person behind the egocentric camera. Given a
single video, our efficient learning-based approach returns the full body 3D
joint positions for each frame. Our method exploits cues from the dynamic
motion signatures of the surrounding scene--which changes predictably as a
function of body pose--as well as static scene structures that reveal the
viewpoint (e.g., sitting vs. standing). We further introduce a novel energy
minimization scheme to infer the pose sequence. It uses soft predictions of the
poses per time instant together with a non-parametric model of human pose
dynamics over longer windows. Our method outperforms an array of possible
alternatives, including deep learning approaches for direct pose regression
from images.

通过利用环境的动态运动和静态构造特征，以及能够推断身体姿态序列的新型最小化能量模型，我们提出了一种高效的基于学习的方法来推断佩戴 egocentric 相机的人体姿态的 3D 关节位置，进一步改进了基于深度学习的图像直接姿态回归等可能的替代方案。