Generating controllable and photorealistic digital human avatars is a long-standing and important problem in Vision and Graphics. Recent methods have shown great progress in terms of either photorealism or inference speed while the combination of the two desired properties still remains unsolved. To this end, we propose a novel method, called DELIFFAS, which parameterizes the appearance of the human as a surface light field that is attached to a controllable and deforming human mesh model. At the core, we represent the light field around the human with a deformable two-surface parameterization, which enables fast and accurate inference of the human appearance. This allows perceptual supervision on the full image compared to previous approaches that could only supervise individual pixels or small patches due to their slow runtime. Our carefully designed human representation and supervision strategy leads to state-of-the-art synthesis results and inference time. The video results and code are available at https://vcai.mpi-inf.mpg.de/projects/DELIFFAS.

翻译：生成可控且照片级逼真的数字人类虚拟化身是视觉与图形学领域中一个长期存在且重要的难题。近期方法在照片真实感或推理速度方面取得了显著进展，但将这两种理想特性相结合的问题仍未得到解决。为此，我们提出了一种名为DELIFFAS的新方法，该方法将人类外观参数化为附着于可控可变形人体网格模型上的表面光场。其核心是通过可变形双表面参数化来表示人体周围的光场，从而实现对人类外观的快速准确推断。与以往因运行缓慢而仅能监督单个像素或小区域的方案相比，我们的方法能够对完整图像进行感知监督。精心设计的人类表征与监督策略带来了当前最先进的合成结果与推理速度。视频结果和代码可在https://vcai.mpi-inf.mpg.de/projects/DELIFFAS获取。