  Dense correspondence between humans carries powerful semantic information
that can be utilized to solve fundamental problems for full-body understanding
such as in-the-wild surface matching, tracking and reconstruction. In this
paper we present BodyMap, a new framework for obtaining high-definition
full-body and continuous dense correspondence between in-the-wild images of
clothed humans and the surface of a 3D template model. The correspondences
cover fine details such as hands and hair, while capturing regions far from the
body surface, such as loose clothing. Prior methods for estimating such dense
surface correspondence i) cut a 3D body into parts which are unwrapped to a 2D
UV space, producing discontinuities along part seams, or ii) use a single
surface for representing the whole body, but none handled body details. Here,
we introduce a novel network architecture with Vision Transformers that learn
fine-level features on a continuous body surface. BodyMap outperforms prior
work on various metrics and datasets, including DensePose-COCO by a large
margin. Furthermore, we show various applications ranging from multi-layer
dense cloth correspondence, neural rendering with novel-view synthesis and
appearance swapping.
