  Recently, regression-based methods have dominated the field of 3D human pose
and shape estimation. Despite their promising results, a common issue is the
misalignment between predictions and image observations, often caused by minor
joint rotation errors that accumulate along the kinematic chain. To address
this issue, we propose to construct dense correspondences between initial human
model estimates and the corresponding images that can be used to refine the
initial predictions. To this end, we utilize renderings of the 3D models to
predict per-pixel 2D displacements between the synthetic renderings and the RGB
images. This allows us to effectively integrate and exploit appearance
information of the persons. Our per-pixel displacements can be efficiently
transformed to per-visible-vertex displacements and then used for 3D model
refinement by minimizing a reprojection loss. To demonstrate the effectiveness
of our approach, we refine the initial 3D human mesh predictions of multiple
models using different refinement procedures on 3DPW and RICH. We show that our
approach not only consistently leads to better image-model alignment, but also
to improved 3D accuracy.
