We present animatable neural radiance fields 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 arbitrary views, and 3) animation of the human with arbitrary poses.

翻译：我们用单视视频为详细的人类血管创造提供了可想象的神经光亮场。我们的方法通过在学习场景展示网络的同时引入清晰的外形制导变形,将神经光亮场扩展到人类运动的动态场景。特别是,我们估计每个框架的人类面貌,并学习详细的人类样板的常态光亮空间,使自然形状从观察空间变形到由表面参数明确控制的金字塔空间。为了弥补不准确的外观估计,我们引入了在学习过程中更新最初面貌的变形精美战略,这不仅有助于学习更准确的人类重建,而且还加快了趋同速度。在实验中,我们表明拟议方法取得了以下成果:(1) 以高质量的细节隐含的人类几何和外观重建;(2) 将人类从任意观点中呈现成光现实,以及(3) 以任意的面貌描绘人类的动画。