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}{this https URL}.

利用几何约束和物理先验来提高动态人体重建的质量和稳定性，实现更准确的图像和新视角合成。

HumanRecon: 利用几何线索和物理先验的动态人体神经重建

HumanRecon: Neural Reconstruction of Dynamic Human Using Geometric Cues  and Physical Priors

In recent years, neural implicit surface reconstruction methods have become
popular for multi-view 3D reconstruction. In contrast to traditional multi-view
stereo methods, these approaches tend to produce smoother and more complete
reconstructions due to the inductive smoothness bias of neural networks.
State-of-the-art neural implicit methods allow for high-quality reconstructions
of simple scenes from many input views. Yet, their performance drops
significantly for larger and more complex scenes and scenes captured from
sparse viewpoints. This is caused primarily by the inherent ambiguity in the
RGB reconstruction loss that does not provide enough constraints, in particular
in less-observed and textureless areas. Motivated by recent advances in the
area of monocular geometry prediction, we systematically explore the utility
these cues provide for improving neural implicit surface reconstruction. We
demonstrate that depth and normal cues, predicted by general-purpose monocular
estimators, significantly improve reconstruction quality and optimization time.
Further, we analyse and investigate multiple design choices for representing
neural implicit surfaces, ranging from monolithic MLP models over single-grid
to multi-resolution grid representations. We observe that geometric monocular
priors improve performance both for small-scale single-object as well as
large-scale multi-object scenes, independent of the choice of representation.

通过 monocular geometry prediction 预测的深度和法向量提示显着提高神经隐式表面重建的质量和优化时间，特别是在大规模复杂场景下，独立于表征的选择。