This paper addresses the challenge of quickly reconstructing free-viewpoint
videos of dynamic humans from sparse multi-view videos. Some recent works
represent the dynamic human as a canonical neural radiance field (NeRF) and a
motion field, which are learned from videos through differentiable rendering.
But the per-scene optimization generally requires hours. Other generalizable
NeRF models leverage learned prior from datasets and reduce the optimization
time by only finetuning on new scenes at the cost of visual fidelity. In this
paper, we propose a novel method for learning neural volumetric videos of
dynamic humans from sparse view videos in minutes with competitive visual
quality. Specifically, we define a novel part-based voxelized human
representation to better distribute the representational power of the network
to different human parts. Furthermore, we propose a novel 2D motion
parameterization scheme to increase the convergence rate of deformation field
learning. Experiments demonstrate that our model can be learned 100 times
faster than prior per-scene optimization methods while being competitive in the
rendering quality. Training our model on a $512 \times 512$ video with 100
frames typically takes about 5 minutes on a single RTX 3090 GPU. The code will
be released on our project page: this https URL

本文提出了一种从稀疏视角视频中学习动态人体神经体积视频的新方法，通过使用新的部分基于体素的人体表示法和二维运动参数化方案来提高模型的表现力和收敛速度，实验表明该模型与传统优化方法相比，在渲染质量上有竞争力，但学习速度更快。