  Rendering moving human bodies at free viewpoints only from a monocular video
is quite a challenging problem. The information is too sparse to model
complicated human body structures and motions from both view and pose
dimensions. Neural radiance fields (NeRF) have shown great power in novel view
synthesis and have been applied to human body rendering. However, most current
NeRF-based methods bear huge costs for both training and rendering, which
impedes the wide applications in real-life scenarios. In this paper, we propose
a rendering framework that can learn moving human body structures extremely
quickly from a monocular video. The framework is built by integrating both
neural fields and neural voxels. Especially, a set of generalizable neural
voxels are constructed. With pretrained on various human bodies, these general
voxels represent a basic skeleton and can provide strong geometric priors. For
the fine-tuning process, individual voxels are constructed for learning
differential textures, complementary to general voxels. Thus learning a novel
body can be further accelerated, taking only a few minutes. Our method shows
significantly higher training efficiency compared with previous methods, while
maintaining similar rendering quality. The project page is at
https://taoranyi.com/gneuvox .
