  We propose a new 3D holistic++ scene understanding problem, which jointly
tackles two tasks from a single-view image: (i) holistic scene parsing and
reconstruction---3D estimations of object bounding boxes, camera pose, and room
layout, and (ii) 3D human pose estimation. The intuition behind is to leverage
the coupled nature of these two tasks to improve the granularity and
performance of scene understanding. We propose to exploit two critical and
essential connections between these two tasks: (i) human-object interaction
(HOI) to model the fine-grained relations between agents and objects in the
scene, and (ii) physical commonsense to model the physical plausibility of the
reconstructed scene. The optimal configuration of the 3D scene, represented by
a parse graph, is inferred using Markov chain Monte Carlo (MCMC), which
efficiently traverses through the non-differentiable joint solution space.
Experimental results demonstrate that the proposed algorithm significantly
improves the performance of the two tasks on three datasets, showing an
improved generalization ability.
