Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only collections of single-view 2D photographs has very recently made much progress. These 3D GANs, however, have not been demonstrated for human bodies and the generated radiance fields of existing frameworks are not directly editable, limiting their applicability in downstream tasks. We propose a solution to these challenges by developing a 3D GAN framework that learns to generate radiance fields of human bodies or faces in a canonical pose and warp them using an explicit deformation field into a desired body pose or facial expression. Using our framework, we demonstrate the first high-quality radiance field generation results for human bodies. Moreover, we show that our deformation-aware training procedure significantly improves the quality of generated bodies or faces when editing their poses or facial expressions compared to a 3D GAN that is not trained with explicit deformations.

翻译：仅利用单视 2D 照片收集的3D-觉醒基因对抗网络(GANs)的未经监督的学习最近取得了很大进展。然而,这些3D GANs还没有为人体展示出来,现有框架产生的光亮领域无法直接编辑,限制了其在下游任务中的适用性。我们建议通过开发一个 3D GAN 框架来应对这些挑战,该框架学习如何产生人体或脸部的光亮区域,并利用明显的变形场将它们扭曲成理想的身体或面部表情。我们利用这个框架展示了人类身体的第一批高品质的发光场生成结果。此外,我们表明,在对产生的身体或面部进行编辑时,我们变形培训程序大大改善了其容或面部的品质,而3D GAN 则没有经过明确的变形培训。