Lightweight creation of 3D digital avatars is a highly desirable but challenging task. With only sparse videos of a person under unknown illumination, we propose a method to create relightable and animatable neural avatars, which can be used to synthesize photorealistic images of humans under novel viewpoints, body poses, and lighting. The key challenge here is to disentangle the geometry, material of the clothed body, and lighting, which becomes more difficult due to the complex geometry and shadow changes caused by body motions. To solve this ill-posed problem, we propose novel techniques to better model the geometry and shadow changes. For geometry change modeling, we propose an invertible deformation field, which helps to solve the inverse skinning problem and leads to better geometry quality. To model the spatial and temporal varying shading cues, we propose a pose-aware part-wise light visibility network to estimate light occlusion. Extensive experiments on synthetic and real datasets show that our approach reconstructs high-quality geometry and generates realistic shadows under different body poses. Code and data are available at \url{https://wenbin-lin.github.io/RelightableAvatar-page/}.

翻译：轻量化创建3D数字虚拟形象是一项极具吸引力但充满挑战的任务。针对未知光照条件下的人物稀疏视频，我们提出了一种方法，用于创建可重照明和可动画化的神经虚拟形象，这些虚拟形象可用于在全新视角、人体姿态及光照条件下合成人物的真实感人像。核心挑战在于解耦着装人体的几何、材质与光照信息，而人体运动导致的复杂几何与阴影变化使这一问题更加困难。为解决这一病态问题，我们提出了新颖的技术手段，以更好地建模几何与阴影变化。在几何变化建模方面，我们提出一种可逆形变场，这有助于解决逆蒙皮问题并获得更优质的几何效果。为模拟空间与时间上变化的着色线索，我们提出一种姿态感知的局部光照可见性网络，用于估计光遮挡。在合成与真实数据集上的大量实验表明，我们的方法能够重建高质量几何，并在不同人体姿态下生成逼真的阴影。代码与数据可在 \url{https://wenbin-lin.github.io/RelightableAvatar-page/} 获取。