  Nerf-based Generative models have shown impressive capacity in generating
high-quality images with consistent 3D geometry. Despite successful synthesis
of fake identity images randomly sampled from latent space, adopting these
models for generating face images of real subjects is still a challenging task
due to its so-called inversion issue. In this paper, we propose a universal
method to surgically fine-tune these NeRF-GAN models in order to achieve
high-fidelity animation of real subjects only by a single image. Given the
optimized latent code for an out-of-domain real image, we employ 2D loss
functions on the rendered image to reduce the identity gap. Furthermore, our
method leverages explicit and implicit 3D regularizations using the in-domain
neighborhood samples around the optimized latent code to remove geometrical and
visual artifacts. Our experiments confirm the effectiveness of our method in
realistic, high-fidelity, and 3D consistent animation of real faces on multiple
NeRF-GAN models across different datasets.
