HumanRecon: Neural Reconstruction of Dynamic Human Using Geometric Cues
  and Physical Priors
Junhui Yin Wei Yin Hao Chen Xuqian Ren Zhanyu Ma Jun Guo Yifan Liu
Recent methods for dynamic human reconstruction have attained promising
reconstruction results. Most of these methods rely only on RGB color
supervision without considering explicit geometric constraints. This leads to
existing human reconstruction techniques being more prone to overfitting to
color and causes geometrically inherent ambiguities, especially in the sparse
multi-view setup.
  Motivated by recent advances in the field of monocular geometry prediction,
we consider the geometric constraints of estimated depth and normals in the
learning of neural implicit representation for dynamic human reconstruction. As
a geometric regularization, this provides reliable yet explicit supervision
information, and improves reconstruction quality. We also exploit several
beneficial physical priors, such as adding noise into view direction and
maximizing the density on the human surface. These priors ensure the color
rendered along rays to be robust to view direction and reduce the inherent
ambiguities of density estimated along rays. Experimental results demonstrate
that depth and normal cues, predicted by human-specific monocular estimators,
can provide effective supervision signals and render more accurate images.
Finally, we also show that the proposed physical priors significantly reduce
overfitting and improve the overall quality of novel view synthesis. Our code
is available
at:~\href{https://github.com/PRIS-CV/HumanRecon}{https://github.com/PRIS-CV/HumanRecon}.