An increasingly common approach for creating photo-realistic digital avatars is through the use of volumetric neural fields. The original neural radiance field (NeRF) allowed for impressive novel view synthesis of static heads when trained on a set of multi-view images, and follow up methods showed that these neural representations can be extended to dynamic avatars. Recently, new variants also surpassed the usual drawback of baked-in illumination in neural representations, showing that static neural avatars can be relit in any environment. In this work we simultaneously tackle both the motion and illumination problem, proposing a new method for relightable and animatable neural heads. Our method builds on a proven dynamic avatar approach based on a mixture of volumetric primitives, combined with a recently-proposed lightweight hardware setup for relightable neural fields, and includes a novel architecture that allows relighting dynamic neural avatars performing unseen expressions in any environment, even with nearfield illumination and viewpoints.

翻译：创建逼真数字头像日益常见的方法是通过使用体积神经场。原始神经辐射场（NeRF）在基于多视角图像集训练后，能够实现令人印象深刻的静态头部新视角合成，后续方法表明这些神经表示可扩展至动态头像。近期新变体也克服了神经表示中固有照明的常见缺陷，证明静态神经头像可在任意环境中重新照明。本文同时攻克运动与照明两大难题，提出一种可重照明与可动画神经头部的新方法。该方法基于已验证的动态头像方案（采用体积基元混合），结合近期提出的轻量级硬件配置实现可重照明神经场，并引入新型架构，允许在任意环境中（甚至包括近场照明与视角）对执行未见表情的动态神经头像进行重照明。