NeRF-GAN Distillation for Efficient 3D-Aware Generation with Convolutions

Pose-conditioned convolutional generative models
struggle with high-quality 3D-consistent image generation
from single-view datasets, due to their lack of sufficient
3D priors. Recently, the integration of Neural Radiance
Fields (NeRFs) and generative models, such as Generative
Adversarial Networks (GANs), has transformed 3D-aware
generation from single-view images. NeRF-GANs exploit
the strong inductive bias of neural 3D representations and
volumetric rendering at the cost of higher computational
complexity. This study aims at revisiting pose-conditioned
2D GANs for efficient 3D-aware generation at inference
time by distilling 3D knowledge from pretrained NeRFGANs.
We propose a simple and effective method, based on
re-using the well-disentangled latent space of a pre-trained
NeRF-GAN in a pose-conditioned convolutional network
to directly generate 3D-consistent images corresponding
to the underlying 3D representations. Experiments on
several datasets demonstrate that the proposed method
obtains results comparable with volumetric rendering in
terms of quality and 3D consistency while benefiting from
the computational advantage of convolutional networks.
The code is available at: https://github.com/
mshahbazi72/NeRF-GAN-Distillation

• Machine Perception

• Machine Intelligence