This paper tackles the challenge of creating relightable and animatable
neural avatars from sparse-view (or even monocular) videos of dynamic humans
under unknown illumination. Compared to studio environments, this setting is
more practical and accessible but poses an extremely challenging ill-posed
problem. Previous neural human reconstruction methods are able to reconstruct
animatable avatars from sparse views using deformed Signed Distance Fields
(SDF) but cannot recover material parameters for relighting. While
differentiable inverse rendering-based methods have succeeded in material
recovery of static objects, it is not straightforward to extend them to dynamic
humans as it is computationally intensive to compute pixel-surface intersection
and light visibility on deformed SDFs for inverse rendering. To solve this
challenge, we propose a Hierarchical Distance Query (HDQ) algorithm to
approximate the world space distances under arbitrary human poses.
Specifically, we estimate coarse distances based on a parametric human model
and compute fine distances by exploiting the local deformation invariance of
SDF. Based on the HDQ algorithm, we leverage sphere tracing to efficiently
estimate the surface intersection and light visibility. This allows us to
develop the first system to recover animatable and relightable neural avatars
from sparse view (or monocular) inputs. Experiments demonstrate that our
approach is able to produce superior results compared to state-of-the-art
methods. Our code will be released for reproducibility.

从稀疏视点（甚至单眼）动态人类视频中的未知照明条件下创建可重新照明和可动画化的神经头像是该论文的主要挑战，为了解决这个问题，作者提出了一种层次化距离查询算法来近似任意人类姿势下的世界空间距离，并基于此算法开发了首个能从稀疏视点（或单眼）输入中恢复出可动画和可重新照明的神经头像的系统，实验证明该方法比现有的方法有着更优秀的结果。