We present animatable neural radiance fields (animatable NeRF) for detailed
human avatar creation from monocular videos. Our approach extends neural
radiance fields (NeRF) to the dynamic scenes with human movements via
introducing explicit pose-guided deformation while learning the scene
representation network. In particular, we estimate the human pose for each
frame and learn a constant canonical space for the detailed human template,
which enables natural shape deformation from the observation space to the
canonical space under the explicit control of the pose parameters. To
compensate for inaccurate pose estimation, we introduce the pose refinement
strategy that updates the initial pose during the learning process, which not
only helps to learn more accurate human reconstruction but also accelerates the
convergence. In experiments we show that the proposed approach achieves 1)
implicit human geometry and appearance reconstruction with high-quality
details, 2) photo-realistic rendering of the human from novel views, and 3)
animation of the human with novel poses.

本文提出了可动态变化神经辐射场 (animatable NeRF) 的方法用于从单眼视屏中创建细节丰富的人物角色，通过引入显式姿势引导变形的方式学习场景表示网络扩展神经辐射场 (NeRF) 到带有人体运动的动态场景，并展示了该方法在人物几何和外观重建、真实感渲染和动画设计等方面取得了突破性成果。