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3D human motion capture from monocular RGB images respecting interactions ofa subject with complex and possibly deformable environments is a verychallenging, ill-posed and under-explored problem. Existing methods address itonly weakly and do not model possible surface deformations often occurring whenhumans interact with scene surfaces. In contrast, this paper proposesMoCapDeform, i.e., a new framework for monocular 3D human motion capture thatis the first to explicitly model non-rigid deformations of a 3D scene forimproved 3D human pose estimation and deformable environment reconstruction.MoCapDeform accepts a monocular RGB video and a 3D scene mesh aligned in thecamera space. It first localises a subject in the input monocular video alongwith dense contact labels using a new raycasting based strategy. Next, ourhuman-environment interaction constraints are leveraged to jointly optimiseglobal 3D human poses and non-rigid surface deformations. MoCapDeform achievessuperior accuracy than competing methods on several datasets, including ournewly recorded one with deforming background scenes.

MoCapDeform: Monocular 3D Human Motion Capture in Deformable Scenes