CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis (2110.09788v1)

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

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

CIPS-3D introduces a novel approach to generating three-dimensional data representations within Generative Adversarial Networks (GANs). The authors propose a methodology grounded in Conditionally-Independent Pixel Synthesis, aiming to enhance performance in generating 3D-aware content. This work contributes to the field by refining generation techniques that incorporate 3D structural understanding without relying heavily on complex architectures or excessive computational resources.

Key Contributions

The primary innovation in CIPS-3D lies in its ability to generate 3D-aware outputs through a simplified yet effective pixel synthesis process. Unlike traditional GAN models, which often require intricate network designs to handle the complexities of 3D space, this approach leverages conditionally-independent synthesis that allows each pixel to be rendered with a degree of autonomy. By doing so, the system can maintain awareness of spatial dimensions without explicitly encoding them through elaborate network adjustments.

Numerical Results and Performance

The paper delineates several metrics where CIPS-3D exhibits substantial improvements over previous models. Experiments conducted show marked advancements in both the visual fidelity and computational efficiency of the generated 3D content. Performance benchmarks indicate a reduction in rendering times, alongside enhanced alignment of generated objects with their expected three-dimensional structures. These quantitative outcomes underscore the efficacy of employing conditionally-independent synthesis in GAN architectures focused on 3D generation.

Implications and Future Directions

The implications of CIPS-3D are significant in several application domains, including augmented reality, computer vision, and virtual environment creation. From a theoretical perspective, this work challenges the prevailing notion that complex models are required for effective 3D generation within GAN frameworks. The concept of pixel-level independence introduces a paradigm shift that could be explored further to optimize other areas of neural synthesis.

Looking ahead, future developments may explore the integration of CIPS-3D with other machine learning paradigms such as reinforcement learning or automated design systems. This could pave the way for more autonomous AI applications capable of creating realistic, interactive 3D environments without extensive human oversight or guidance.

Conclusion

CIPS-3D presents a significant contribution to the field of 3D-aware GANs, with its use of Conditionally-Independent Pixel Synthesis offering both practical and theoretical advancements. The approach underscores the potential for more efficient, streamlined methods in producing high-quality 3D representations, opening new avenues for research and application in AI-driven content creation.