CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis

TL;DR

CIPS-3D introduces a novel 3D-aware GAN generator combining a shallow NeRF and deep implicit neural networks, achieving high-quality, controllable image synthesis from single-view images.

Contribution

It presents a new style-based 3D-aware generator that independently synthesizes pixels and addresses mirror symmetry issues, setting new benchmarks in 3D-aware image synthesis.

Findings

Achieves state-of-the-art FID of 6.97 on FFHQ at 256x256 resolution.

Successfully demonstrates transfer learning and 3D-aware face stylization.

Outperforms previous 3D-aware GANs in image quality and controllability.

Abstract

The style-based GAN (StyleGAN) architecture achieved state-of-the-art results for generating high-quality images, but it lacks explicit and precise control over camera poses. The recently proposed NeRF-based GANs made great progress towards 3D-aware generators, but they are unable to generate high-quality images yet. This paper presents CIPS-3D, a style-based, 3D-aware generator that is composed of a shallow NeRF network and a deep implicit neural representation (INR) network. The generator synthesizes each pixel value independently without any spatial convolution or upsampling operation. In addition, we diagnose the problem of mirror symmetry that implies a suboptimal solution and solve it by introducing an auxiliary discriminator. Trained on raw, single-view images, CIPS-3D sets new records for 3D-aware image synthesis with an impressive FID of 6.97 for images at the $256\times256$ resolution on FFHQ. We also demonstrate several interesting directions for CIPS-3D such as transfer learning and 3D-aware face stylization. The synthesis results are best viewed as videos, so we recommend the readers to check our github project at https://github.com/PeterouZh/CIPS-3D