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Over the years, 2D GANs have achieved great successes in photorealisticportrait generation. However, they lack 3D understanding in the generationprocess, thus they suffer from multi-view inconsistency problem. To alleviatethe issue, many 3D-aware GANs have been proposed and shown notable results, but3D GANs struggle with editing semantic attributes. The controllability andinterpretability of 3D GANs have not been much explored. In this work, wepropose two solutions to overcome these weaknesses of 2D GANs and 3D-awareGANs. We first introduce a novel 3D-aware GAN, SURF-GAN, which is capable ofdiscovering semantic attributes during training and controlling them in anunsupervised manner. After that, we inject the prior of SURF-GAN into StyleGANto obtain a high-fidelity 3D-controllable generator. Unlike existinglatent-based methods allowing implicit pose control, the proposed3D-controllable StyleGAN enables explicit pose control over portraitgeneration. This distillation allows direct compatibility between 3D controland many StyleGAN-based techniques (e.g., inversion and stylization), and alsobrings an advantage in terms of computational resources. Our codes areavailable at https://github.com/jgkwak95/SURF-GAN.

Injecting 3D Perception of Controllable NeRF-GAN into StyleGAN for Editable Portrait Image Synthesis