对艺术家友好的可重光照且可动画化神经头部
Artist-Friendly Relightable and Animatable Neural Heads
AI总结：
本文提出一种对艺术家友好的可重光照、可动画化神经头部方法，融合体积基元动态化身与轻量级重光照采集方案，支持未见表情在任意环境乃至近场条件下重光照。
摘要：
创建照片级真实数字化身的一种日益常见的方法是使用体积神经场。最初的神经辐射场（NeRF）在一组多视图图像上训练时，能够实现令人印象深刻的静态头部新视图合成，而后续方法表明，这些神经表示可以扩展到动态化身。最近，新的变体还克服了神经表示中光照被烘焙进去的常见缺陷，表明静态神经化身可以在任意环境中被重光照。在本工作中，我们同时处理运动和光照问题，提出了一种用于可重光照且可动画化神经头部的新方法。我们的方法建立在一种经过验证的、基于体积基元混合的动态化身方法之上，并结合了最近提出的用于可重光照神经场的轻量级硬件设置；此外还包含一种新颖架构，使得正在执行未见表情的动态神经化身能够在任意环境中进行重光照，即使在近场光照和视点条件下也是如此。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
