We consider the problem of estimating a parametric model of 3D human mesh
from a single image. While there has been substantial recent progress in this
area with direct regression of model parameters, these methods only implicitly
exploit the human body kinematic structure, leading to sub-optimal use of the
model prior. In this work, we address this gap by proposing a new technique for
regression of human parametric model that is explicitly informed by the known
hierarchical structure, including joint interdependencies of the model. This
results in a strong prior-informed design of the regressor architecture and an
associated hierarchical optimization that is flexible to be used in conjunction
with the current standard frameworks for 3D human mesh recovery. We demonstrate
these aspects by means of extensive experiments on standard benchmark datasets,
showing how our proposed new design outperforms several existing and popular
methods, establishing new state-of-the-art results. By considering joint
interdependencies, our method is equipped to infer joints even under data
corruptions, which we demonstrate by conducting experiments under varying
degrees of occlusion.

提出了一种基于已知层次结构的人体参数化模型回归新技术，旨在更加灵活应用于当前标准的三维人体网格恢复框架，并通过实验数据证明其性能优于现有方法，能在数据损坏下推断关节。