NeRFInvertor: High Fidelity NeRF-GAN Inversion for Single-shot Real Image Animation

• URL: http://arxiv.org/abs/2211.17235v1
• Date: Wed, 30 Nov 2022 18:36:45 GMT
• Title: NeRFInvertor: High Fidelity NeRF-GAN Inversion for Single-shot Real Image Animation
• Authors: Yu Yin, Kamran Ghasedi, HsiangTao Wu, Jiaolong Yang, Xin Tong, Yun Fu
• Abstract summary: Nerf-based Generative models have shown impressive capacity in generating high-quality images with consistent 3D geometry. 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.
• Score: 66.0838349951456
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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