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

本文介绍了CIPS-3D，一个基于StyleGAN和NeRF的生成器，它能够生成具有3D感知能力的高品质图像。该生成器避免了空间卷积或上采样，通过深度内隐神经表示网络合成每个像素值，并通过辅助鉴别器解决了镜像对称问题。 在单视图图像上进行训练后，CIPS-3D在FFHQ数据集上以6.97的FID成为3D感知图像合成的最佳记录，同时展示了多个有趣的方向，如迁移学习和3D感知面部风格化。

CIPS-3D:基于条件独立像素合成的3D感知GAN生成器