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Holistic 3D human-scene reconstruction is a crucial and emerging researcharea in robot perception. A key challenge in holistic 3D human-scenereconstruction is to generate a physically plausible 3D scene from a singlemonocular RGB image. The existing research mainly proposes optimization-basedapproaches for reconstructing the scene from a sequence of RGB frames withexplicitly defined physical laws and constraints between different sceneelements (humans and objects). However, it is hard to explicitly define andmodel every physical law in every scenario. This paper proposes using animplicit feature representation of the scene elements to distinguish aphysically plausible alignment of humans and objects from an implausible one.We propose using a graph-based holistic representation with an encoded physicalrepresentation of the scene to analyze the human-object and object-objectinteractions within the scene. Using this graphical representation, weadversarially train our model to learn the feasible alignments of the sceneelements from the training data itself without explicitly defining the laws andconstraints between them. Unlike the existing inference-time optimization-basedapproaches, we use this adversarially trained model to produce a per-frame 3Dreconstruction of the scene that abides by the physical laws and constraints.Our learning-based method achieves comparable 3D reconstruction quality toexisting optimization-based holistic human-scene reconstruction methods anddoes not need inference time optimization. This makes it better suited whencompared to existing methods, for potential use in robotic applications, suchas robot navigation, etc.

Physically Plausible 3D Human-Scene Reconstruction from Monocular RGB Image using an Adversarial Learning Approach