In this paper, we propose a rendering framework that can learn moving human body structures extremely quickly from a monocular video, named as GNeuVox. 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.
@article{gneuvox,
title={Generalizable Neural Voxels for Fast Human Radiance Fields},
author={Taoran Yi and Jiemin Fang and Xinggang Wang and Wenyu Liu},
journal={arxiv:2303.15387},
year={2023}
}