  Capturing the dynamically deforming 3D shape of clothed human is essential
for numerous applications, including VR/AR, autonomous driving, and
human-computer interaction. Existing methods either require a highly
specialized capturing setup, such as expensive multi-view imaging systems, or
they lack robustness to challenging body poses. In this work, we propose a
method capable of capturing the dynamic 3D human shape from a monocular video
featuring challenging body poses, without any additional input. We first build
a 3D template human model of the subject based on a learned regression model.
We then track this template model's deformation under challenging body
articulations based on 2D image observations. Our method outperforms
state-of-the-art methods on an in-the-wild human video dataset 3DPW. Moreover,
we demonstrate its efficacy in robustness and generalizability on videos from
iPER datasets.
