BCNet: Learning Body and Cloth Shape from A Single Image

TL;DR

BCNet introduces a layered garment model on top of SMPL that independently learns garment skinning weights, enabling more accurate and flexible 3D garment and body shape reconstruction from a single image.

Contribution

The paper proposes a novel layered garment representation with independent skinning weights, supporting more garment categories and improving geometry accuracy.

Findings

Supports more garment categories than previous methods.

Achieves more accurate geometry reconstruction.

Enables applications like re-pose and garment transfer.

Abstract

In this paper, we consider the problem to automatically reconstruct garment and body shapes from a single near-front view RGB image. To this end, we propose a layered garment representation on top of SMPL and novelly make the skinning weight of garment independent of the body mesh, which significantly improves the expression ability of our garment model. Compared with existing methods, our method can support more garment categories and recover more accurate geometry. To train our model, we construct two large scale datasets with ground truth body and garment geometries as well as paired color images. Compared with single mesh or non-parametric representation, our method can achieve more flexible control with separate meshes, makes applications like re-pose, garment transfer, and garment texture mapping possible. Code and some data is available at https://github.com/jby1993/BCNet.