  To understand and analyze human behavior, we need to capture humans moving
in, and interacting with, the world. Most existing methods perform 3D human
pose estimation without explicitly considering the scene. We observe however
that the world constrains the body and vice-versa. To motivate this, we show
that current 3D human pose estimation methods produce results that are not
consistent with the 3D scene. Our key contribution is to exploit static 3D
scene structure to better estimate human pose from monocular images. The method
enforces Proximal Relationships with Object eXclusion and is called PROX. To
test this, we collect a new dataset composed of 12 different 3D scenes and RGB
sequences of 20 subjects moving in and interacting with the scenes. We
represent human pose using the 3D human body model SMPL-X and extend SMPLify-X
to estimate body pose using scene constraints. We make use of the 3D scene
information by formulating two main constraints. The inter-penetration
constraint penalizes intersection between the body model and the surrounding 3D
scene. The contact constraint encourages specific parts of the body to be in
contact with scene surfaces if they are close enough in distance and
orientation. For quantitative evaluation we capture a separate dataset with 180
RGB frames in which the ground-truth body pose is estimated using a motion
capture system. We show quantitatively that introducing scene constraints
significantly reduces 3D joint error and vertex error. Our code and data are
available for research at https://prox.is.tue.mpg.de.
