Multiview neural human prediction using implicit differentiable renderer for facial expression, body pose shape and clothes performance capture

Abstract

A neural human performance capture framework (MVS-PERF) captures the skeleton, body shape and clothes displacement, and appearance of a person from a set of calibrated multiview images. It addresses the ambiguity of predicting the absolute position in monocular human mesh recovery, and bridges the volumetric representation from NeRF to animation-friendly performance capture. MVS-PERF includes three modules to extract feature maps from multiview images and fuse them to a feature volume, regress the feature volume to a naked human parameters vector, generating an SMPL-X skin-tight body mesh with a skeletal pose, body shape, and expression, and leverage a neural radiance field and a deformation field to infer the clothes as the displacement on the naked body using differentiable rendering. Clothed body mesh is obtained by adding the interpolated displacement vectors to the SMPL-X skin-tight body mesh vertices. The obtained radiance field is used for free-view volumetric rendering of the input subject.