Artist-Friendly Relightable and Animatable Neural Heads

Yingyan Xu, Prashanth Chandran, Sebastian Weiss, Markus Gross, Gaspard Zoss, Derek Bradley; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 2457-2467

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.