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We propose a new 3D holistic++ scene understanding problem, which jointlytackles two tasks from a single-view image: (i) holistic scene parsing andreconstruction---3D estimations of object bounding boxes, camera pose, and roomlayout, and (ii) 3D human pose estimation. The intuition behind is to leveragethe coupled nature of these two tasks to improve the granularity andperformance of scene understanding. We propose to exploit two critical andessential connections between these two tasks: (i) human-object interaction(HOI) to model the fine-grained relations between agents and objects in thescene, and (ii) physical commonsense to model the physical plausibility of thereconstructed scene. The optimal configuration of the 3D scene, represented bya parse graph, is inferred using Markov chain Monte Carlo (MCMC), whichefficiently traverses through the non-differentiable joint solution space.Experimental results demonstrate that the proposed algorithm significantlyimproves the performance of the two tasks on three datasets, showing animproved generalization ability.

Holistic++ Scene Understanding: Single-view 3D Holistic Scene Parsing  and Human Pose Estimation with Human-Object Interaction and Physical  Commonsense