Balanced parametric body prior for implicit clothed human reconstruction from a monocular RGB

The authors study the problem of reconstructing detailed 3D human surfaces in various poses and clothing from images. The parametric human body allows accurate 3D clothed human reconstruction. However, the offset of large and loose clothing from the inferred parametric body mesh confines the generalisation of the existing parametric body-based methods. A distinctive method that simultaneously generalises well to unseen poses and unseen clothing is proposed. The authors first discover the unbalanced nature of existing implicit function-based methods. To address this issue, the authors propose to synthesise the balanced training samples with a new dependency coefficient in training. The dependency coefficient can tell the network whether the prior from the parametric body model is reliable. The authors then design a novel positional embedding-based attenuation strategy to incorporate the dependency coefficient into the implicit function (IF) network. Comprehensive experiments are conducted on the CAPE dataset to study the effectiveness of the authors’ approach. The proposed method significantly surpasses state-of-the-art approaches and generalises well on unseen poses and clothing. As an illustrative example, the proposed method improves the Chamfer Distance Error and Normal Error by 38.2% and 57.6%.