REGIR: Refined Geometry for Single-Image Implicit Clothed Human Reconstruction

Abstract

Recently, implicit function-based approaches have advanced 3D human reconstruction from a single-view image. However, previous methods suffer from issues such as noisy artifacts, loss of geometric details, and broken limbs under the scenarios of challenging poses. To address these problems, a novel end-to-end deep neural network named ReGIR is proposed, which is a multi-level architecture combining the parametric model with implicit function. The architecture consists of a coarse level and a fine level, and for each level, normal maps and the signed distance function (SDF) are introduced to encode query points. Furthermore, the network is trained in a coarse-to-fine manner to enable robust human body reconstruction with geometric details. Our extensive qualitative and quantitative experiments demonstrate that ReGIR achieves competitive reconstruction results.