Modern 3D-GANs synthesize geometry and texture by training on large-scale datasets with a consistent structure. Training such models on stylized, artistic data, with often unknown, highly variable geometry, and camera information has not yet been shown possible. Can we train a 3D GAN on such artistic data, while maintaining multi-view consistency and texture quality? To this end, we propose an adaptation framework, where the source domain is a pre-trained 3D-GAN, while the target domain is a 2D-GAN trained on artistic datasets. We then distill the knowledge from a 2D generator to the source 3D generator. To do that, we first propose an optimization-based method to align the distributions of camera parameters across domains. Second, we propose regularizations necessary to learn high-quality texture, while avoiding degenerate geometric solutions, such as flat shapes. Third, we show a deformation-based technique for modeling exaggerated geometry of artistic domains, enabling -- as a byproduct -- personalized geometric editing. Finally, we propose a novel inversion method for 3D-GANs linking the latent spaces of the source and the target domains. Our contributions -- for the first time -- allow for the generation, editing, and animation of personalized artistic 3D avatars on artistic datasets.

本文提出了一种适应性框架，它能够使3D-GAN在艺术数据上训练，同时保持多视角的一致性和良好的纹理质量，通过优化方法对摄像机参数进行分布对齐，提出了纹理常规化技术，同时避免退化几何解，并展示了一种变形技术，使艺术领域的几何形态更加夸张，可以个性化的进行几何编辑，最终实现了在艺术数据集上生成、编辑和动画化个性化的3D角色。

3DAvatarGAN: 搭建领域以获得可个性化编辑的头像