Over the years, 2D GANs have achieved great successes in photorealistic
portrait generation. However, they lack 3D understanding in the generation
process, thus they suffer from multi-view inconsistency problem. To alleviate
the issue, many 3D-aware GANs have been proposed and shown notable results, but
3D GANs struggle with editing semantic attributes. The controllability and
interpretability of 3D GANs have not been much explored. In this work, we
propose two solutions to overcome these weaknesses of 2D GANs and 3D-aware
GANs. We first introduce a novel 3D-aware GAN, SURF-GAN, which is capable of
discovering semantic attributes during training and controlling them in an
unsupervised manner. After that, we inject the prior of SURF-GAN into StyleGAN
to obtain a high-fidelity 3D-controllable generator. Unlike existing
latent-based methods allowing implicit pose control, the proposed
3D-controllable StyleGAN enables explicit pose control over portrait
generation. This distillation allows direct compatibility between 3D control
and many StyleGAN-based techniques (e.g., inversion and stylization), and also
brings an advantage in terms of computational resources. Our codes are
available at this https URL

通过引入 SURF-GAN 和将其先验注入 StyleGAN，提出了一种新的三维可控生成器，可进行人像生成的明确姿态控制。