Injecting 3D Perception of Controllable NeRF-GAN into StyleGAN for Editable Portrait Image Synthesis (2207.10257v2)

Abstract: 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 https://github.com/jgkwak95/SURF-GAN.

• The paper presents SURF-GAN, a novel 3D-aware GAN that discovers semantic attributes, enabling unsupervised control during portrait synthesis.
• It integrates neural radiance fields into StyleGAN to produce photorealistic images with explicit, accurate pose control over multiple views.
• The approach bridges 2D and 3D methods, enhancing image consistency and efficiency for applications in AR and interactive virtual avatar creation.

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

The paper by Jeong-gi Kwak et al. explores merging 3D-aware neural radiance fields (NeRF) within a generative adversarial network (GAN) framework to incorporate explicit 3D perception in portrait image synthesis, addressing significant limitations in both 2D and 3D generative models.

Structure and Methodology

1. Identification of Limitations:
   • 2D GANs, while advanced in generating high-resolution, photorealistic images, lack a fundamental understanding of 3D structure, leading to the problem of multi-view inconsistency in generated images.
   • 3D-aware GANs have improved 3D consistency but are weak in controlling semantic attributes due to insufficient exploration of their latent spaces.
2. Proposed Solutions:
   • SURF-GAN: A novel 3D-aware GAN using a neural radiance field-based generator that discovers semantic attributes during training. It permits unsupervised control of these attributes, advancing the controllability and interpretability of 3D-aware GANs.
   • Integration with StyleGAN: By injecting the 3D priors from SURF-GAN into StyleGAN, the authors develop a model capable of high-fidelity 3D-controllable portrait generation. This approach allows seamless integration with existing StyleGAN techniques, such as inversion and stylization, while remaining computationally efficient.
3. Explicit Pose Control:
   • Unlike traditional methods that facilitate implicit pose adjustments via latent space manipulations, the proposed technique achieves explicit pose control. This explicit control enhances the capacity for generating consistent and accurately posed images from multiple views.

Strong Results and Contributions

• High-Fidelity Image Synthesis: The integration realizes more photorealistic and visually consistent outputs compared to prior methods like PiGAN and CIPS-3D, evidenced by superior Fréchet Inception Distance (FID) scores.
• Semantic Controllability in 3D: SURF-GAN's ability to modulate semantic characteristics in an interpretable manner distinguishes it from other 3D representations, where such control has been challenging without explicit supervision.
• Efficiency and Compatibility: By harnessing StyleGAN's architecture alongside 3D awareness, the modeling process remains resource-efficient and compatible with previous works focused on 2D latent manipulations.

Implications and Future Directions

The research paves the way for broader applications in interactive tools necessitating controllable image generation, such as virtual avatars and augmented reality. Additionally, its contributions highlight a direction towards bridging the gap between 2D photorealism and 3D consistency without external 3D supervision.

Future work could address further optimization to manage computational demands at high resolutions inherent in pure NeRF-based solutions. Also, exploring different architectures for higher-dimensional representations might open new possibilities in perceptually grounded 3D image synthesis. Integration with temporal models could also extend this research into video synthesis, managing artifacts typical in GAN-generated videos.

Overall, the paper substantiates the potential of employing 3D generative models with discoverable semantic modulations, integrating benefits from both 2D and 3D spaces. This dual-angle approach could innovate numerous fields reliant on adaptable and realistic visual content generation.