  Nonparametric based methods have recently shown promising results in
reconstructing human bodies from monocular images while model-based methods can
help correct these estimates and improve prediction. However, estimating model
parameters from global image features may lead to noticeable misalignment
between the estimated meshes and image evidence. To address this issue and
leverage the best of both worlds, we propose a framework of three consecutive
modules. A dense map prediction module explicitly establishes the dense UV
correspondence between the image evidence and each part of the body model. The
inverse kinematics module refines the key point prediction and generates a
posed template mesh. Finally, a UV inpainting module relies on the
corresponding feature, prediction and the posed template, and completes the
predictions of occluded body shape. Our framework leverages the best of
non-parametric and model-based methods and is also robust to partial occlusion.
Experiments demonstrate that our framework outperforms existing 3D human
estimation methods on multiple public benchmarks.
