  The 3D world limits the human body pose and the human body pose conveys
information about the surrounding objects. Indeed, from a single image of a
person placed in an indoor scene, we as humans are adept at resolving
ambiguities of the human pose and room layout through our knowledge of the
physical laws and prior perception of the plausible object and human poses.
However, few computer vision models fully leverage this fact. In this work, we
propose an end-to-end trainable model that perceives the 3D scene from a single
RGB image, estimates the camera pose and the room layout, and reconstructs both
human body and object meshes. By imposing a set of comprehensive and
sophisticated losses on all aspects of the estimations, we show that our model
outperforms existing human body mesh methods and indoor scene reconstruction
methods. To the best of our knowledge, this is the first model that outputs
both object and human predictions at the mesh level, and performs joint
optimization on the scene and human poses.
