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.

翻译：Nerf-基于Nerf-GAN模型在以一致的 3D 几何方法生成高质量图像方面表现出令人印象深刻的能力。 尽管成功合成了从潜在空间随机抽样的假身份图像,但采用这些模型生成真实对象的面部图像仍因其所谓的反向问题而是一项具有挑战性的任务。 在本文中,我们提出了一个通用方法来对NeRF-GAN模型进行外科微调,以便只用一个单一图像实现真实对象的高度虚度动画。鉴于外科真实图像的优化潜伏代码,我们使用2D 丢失功能来缩小身份差距。 此外,我们的方法利用最佳潜伏代码周围的内部社区样本,明确和隐含的3D正规化作用来清除几何和视觉文物。我们的实验证实了我们在不同数据集多个 NERF-GAN模型上真实面部的实用性、高度和3D一致性动画的效果。