DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Model

Figure 1. Our DATID-3D succeeded in domain adaptation of 3D-aware generative models without additional data for the target domain while preserving diversity that is inherent in the text prompt as well as enabling high-quality pose-controlled image synthesis with excellent textimage correspondence. However, StyleGAN-NADA * , a 3D extension of the state-of-the-art StyleGAN-NADA for 2D generative models [20], yielded alike images in style with poor text-image correspondence. See the supplementary videos at gwang-kim.github.io/datid_3d.