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.

我们提出使用密集对应将人体模型的初步估计与相应的图像生成关联，以便优化初步估计的过程。我们将 3D 模型的渲染结果用于预测合成图像与 RGB 图像之间的像素级 2D 位移，从而有效地整合和利用人体外观信息。通过最小化投影损失，我们可以将像素级位移转化为可见顶点级位移，并用于三维模型的优化。在 3DPW 和 RICH 上，我们通过不同的优化过程对多个模型的初步估计进行了优化，结果表明我们的方法不仅能够使图像与模型更好地对齐，还能提高三维的准确性。