3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data

We consider the problem of obtaining dense 3D reconstructions of humans fromsingle and partially occluded views. In such cases, the visual evidence isusually insufficient to identify a 3D reconstruction uniquely, so we aim atrecovering several plausible reconstructions compatible with the input data. Wesuggest that ambiguities can be modelled more effectively by parametrizing thepossible body shapes and poses via a suitable 3D model, such as SMPL forhumans. We propose to learn a multi-hypothesis neural network regressor using abest-of-M loss, where each of the M hypotheses is constrained to lie on amanifold of plausible human poses by means of a generative model. We show thatour method outperforms alternative approaches in ambiguous pose recovery onstandard benchmarks for 3D humans, and in heavily occluded versions of thesebenchmarks.