  Inspired by the success of volumetric 3D pose estimation, some recent human
mesh estimators propose to estimate 3D skeletons as intermediate
representations, from which, the dense 3D meshes are regressed by exploiting
the mesh topology. However, body shape information is lost in extracting
skeletons, leading to mediocre performance. The advanced motion capture systems
solve the problem by placing dense physical markers on the body surface, which
allows to extract realistic meshes from their non-rigid motions. However, they
cannot be applied to wild images without markers. In this work, we present an
intermediate representation, named virtual markers, which learns 64 landmark
keypoints on the body surface based on the large-scale mocap data in a
generative style, mimicking the effects of physical markers. The virtual
markers can be accurately detected from wild images and can reconstruct the
intact meshes with realistic shapes by simple interpolation. Our approach
outperforms the state-of-the-art methods on three datasets. In particular, it
surpasses the existing methods by a notable margin on the SURREAL dataset,
which has diverse body shapes. Code is available at
https://github.com/ShirleyMaxx/VirtualMarker
