StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis

Abstract: We propose StyleNeRF, a 3D-aware generative model for photo-realistic high-resolution image synthesis with high multi-view consistency, which can be trained on unstructured 2D images. Existing approaches either cannot synthesize high-resolution images with fine details or yield noticeable 3D-inconsistent artifacts. In addition, many of them lack control over style attributes and explicit 3D camera poses. StyleNeRF integrates the neural radiance field (NeRF) into a style-based generator to tackle the aforementioned challenges, i.e., improving rendering efficiency and 3D consistency for high-resolution image generation. We perform volume rendering only to produce a low-resolution feature map and progressively apply upsampling in 2D to address the first issue. To mitigate the inconsistencies caused by 2D upsampling, we propose multiple designs, including a better upsampler and a new regularization loss. With these designs, StyleNeRF can synthesize high-resolution images at interactive rates while preserving 3D consistency at high quality. StyleNeRF also enables control of camera poses and different levels of styles, which can generalize to unseen views. It also supports challenging tasks, including zoom-in and-out, style mixing, inversion, and semantic editing.

• The paper introduces StyleNeRF, a novel 3D-aware generator that integrates NeRF into a style-based framework for high-resolution image synthesis.
• It employs a unique upsampling strategy and regularization loss to achieve interactive rendering speeds while maintaining multi-view consistency.
• Empirical evaluations on datasets like FFHQ and MetFaces demonstrate its superior image quality and efficiency compared to methods such as HoloGAN and GRAF.

Overview of StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis

The paper introduces StyleNeRF, a novel 3D-aware generative model designed to synthesize high-resolution, photorealistic images with robust multi-view consistency. Unlike existing approaches, StyleNeRF achieves fine detail in image synthesis while minimizing 3D-inconsistent artifacts, operating on unstructured 2D image data.

Model Design and Innovations

StyleNeRF innovatively integrates the neural radiance field (NeRF) into a style-based generator. By adopting NeRF, StyleNeRF offers explicit control over 3D camera poses and style attributes. The model addresses key challenges inherent in prior methods: inadequate detail rendering and lack of 3D consistency. StyleNeRF improves rendering efficiency and 3D consistency by employing volume rendering to produce low-resolution feature maps followed by progressive upsampling in 2D.

Key innovations include a novel upsampling strategy and a regularization loss to maintain 3D consistency during high-resolution image synthesis. These designs enhance rendering efficiency, enabling interactive image generation rates without compromising on multi-view consistency.

Methodological Implementation

The methodology revolves around approximating typical NeRF operations for high-resolution imagery. A critical modification is early aggregation in 2D space, streamlining computation and facilitating progressive resolution increases. This is achieved by using fewer channels at higher resolutions, a departure from traditional high-cost volume rendering.

Attention is also given to designing an upsampler that balances consistency and visual fidelity, tackling the artifacts common in pixel-wise learnable and bilinear interpolation methods. To reinforce multi-view consistency, the authors introduce a novel regularization approach that ties the outputs closely to the NeRF paths.

Empirical Evaluation

StyleNeRF's efficacy is corroborated through rigorous experiments on datasets such as FFHQ, MetFaces, AFHQ, and CompCars. Comparative analyses against HoloGAN, GRAF, and $\pi$-GAN evidence its superior image quality and consistency, as measured by FID and KID metrics.

Interactive rendering speeds are an added advantage, with StyleNeRF achieving significant performance improvements over voxel-based and pure NeRF methods. This positions StyleNeRF as a practical choice for applications requiring responsive generation of high-quality, 3D-consistent images.

Practical and Theoretical Implications

The proposed model not only advances state-of-the-art in 3D-aware image synthesis but also provides a scalable framework for practitioners in fields such as virtual reality and gaming. The ability to control style and camera parameters opens avenues for creative and production applications.

Theoretically, StyleNeRF prompts further exploration into optimizing feature space aggregation and designing neural networks with inherent multi-view consistency. This research holds potential implications for future AI developments, focusing on efficiency and quality balance in 3D generative models.

Conclusion

StyleNeRF presents a significant contribution to the domain of high-fidelity, 3D-consistent image generation. It bridges the gap between generative quality and interactive rendering capabilities, setting a foundation for subsequent innovations in the field. Future research directions could investigate deeper structural consistency within neural representations, enhancing both theoretical understanding and practical applications of 3D-aware generative models.