  Despite the impressive results achieved by deep learning based 3D
reconstruction, the techniques of directly learning to model 4D human captures
with detailed geometry have been less studied. This work presents a novel
framework that can effectively learn a compact and compositional representation
for dynamic human by exploiting the human body prior from the widely used SMPL
parametric model. Particularly, our representation, named H4D, represents a
dynamic 3D human over a temporal span with the SMPL parameters of shape and
initial pose, and latent codes encoding motion and auxiliary information. A
simple yet effective linear motion model is proposed to provide a rough and
regularized motion estimation, followed by per-frame compensation for pose and
geometry details with the residual encoded in the auxiliary code. Technically,
we introduce novel GRU-based architectures to facilitate learning and improve
the representation capability. Extensive experiments demonstrate our method is
not only efficacy in recovering dynamic human with accurate motion and detailed
geometry, but also amenable to various 4D human related tasks, including motion
retargeting, motion completion and future prediction. Please check out the
project page for video and code: https://boyanjiang.github.io/H4D/.
