GSGAN: Adversarial Learning for Hierarchical Generation of 3D Gaussian Splats (2406.02968v2)

Abstract: Most advances in 3D Generative Adversarial Networks (3D GANs) largely depend on ray casting-based volume rendering, which incurs demanding rendering costs. One promising alternative is rasterization-based 3D Gaussian Splatting (3D-GS), providing a much faster rendering speed and explicit 3D representation. In this paper, we exploit Gaussian as a 3D representation for 3D GANs by leveraging its efficient and explicit characteristics. However, in an adversarial framework, we observe that a na\"ive generator architecture suffers from training instability and lacks the capability to adjust the scale of Gaussians. This leads to model divergence and visual artifacts due to the absence of proper guidance for initialized positions of Gaussians and densification to manage their scales adaptively. To address these issues, we introduce a generator architecture with a hierarchical multi-scale Gaussian representation that effectively regularizes the position and scale of generated Gaussians. Specifically, we design a hierarchy of Gaussians where finer-level Gaussians are parameterized by their coarser-level counterparts; the position of finer-level Gaussians would be located near their coarser-level counterparts, and the scale would monotonically decrease as the level becomes finer, modeling both coarse and fine details of the 3D scene. Experimental results demonstrate that ours achieves a significantly faster rendering speed (x100) compared to state-of-the-art 3D consistent GANs with comparable 3D generation capability. Project page: https://hse1032.github.io/gsgan.

• The paper introduces a hierarchical multi-scale Gaussian representation that effectively models both coarse and fine 3D details.
• It presents an enhanced generator architecture that stabilizes training by regularizing Gaussian positions and scales.
• The method achieves approximately 100x faster rendering while maintaining competitive FID scores on high-resolution datasets.

Adversarial Generation of Hierarchical Gaussians for 3D Generative Models

This paper presents a novel approach to enhancing 3D Generative Adversarial Networks (3D GANs) through an efficient 3D representation termed hierarchical Gaussian splatting (3D-GS). The approach addresses computational inefficiencies inherent in traditional ray-casting volume rendering methods commonly employed in 3D GANs, which impede high-resolution image rendering. By leveraging the rasterization-based 3D Gaussian splatting technique for 3D representation, the authors aim to achieve more efficient and explicit 3D modeling and rendering capabilities.

Key Contributions

The paper introduces several innovations:

1. Hierarchical Multi-Scale Gaussian Representation: The authors propose a hierarchical representation where finer-level Gaussians are parameterized by their coarser-level counterparts. This arrangement models both coarse and fine details of a 3D scene, ensuring that the hierarchical Gaussians provide detailed and nuanced 3D representations.
2. Enhanced Generator Architecture: A new generator architecture is introduced to accommodate the hierarchical Gaussian representation. This architecture regularizes the position and scale of generated Gaussians, effectively addressing issues like training instability and imprecise adjustments of the Gaussian scales.
3. Efficiency in Rendering: The proposed method achieves significantly faster rendering speeds—approximately 100 times faster—compared to state-of-the-art 3D consistent GANs while maintaining comparable generation quality.

Experimental Evaluation

The authors rigorously evaluate their method on FFHQ and AFHQ-Cat datasets with resolutions of 256x256 and 512x512. Key quantitative metrics, such as FID-50K-full scores and rendering speed, are used to benchmark the model’s performance. The proposed method achieves FID scores of 6.59 (FFHQ-256), 5.60 (FFHQ-512), 3.43 (AFHQ-Cat-256), and 3.79 (AFHQ-Cat-512). These scores demonstrate that the method competes effectively with or surpasses existing state-of-the-art methods in the field.

In terms of rendering speed, the method achieves a rendering time of 2.7 ms for 256x256 resolution and 3.0 ms for 512x512 resolution, demonstrating its computational efficiency over baseline models. This significant reduction in rendering time highlights the practical implications of using rasterization-based Gaussian representations for high-resolution 3D generative tasks.

Qualitative Assessments and 3D Consistency

The qualitative results show that the proposed method generates images with consistent multi-view properties, modeling both coarse and fine details effectively. Notably, the method achieves superior 3D consistency in generated images when compared with recent 3D consistent GANs, as validated through metrics like PSNR and SSIM.

Theoretical and Practical Implications

This work implies that hierarchical Gaussian representations can be an efficient alternative to traditional volume rendering methods in 3D GANs, bringing notable improvements in training stability and rendering speed without compromising generation quality. Hierarchical structures ensure that fine details are effectively modeled, making it an appealing solution for high-resolution generative tasks.

Future Directions

Future research could explore the adaptive introduction and removal of Gaussians, enhancing the flexibility of the method in modeling various complexities of scene details. Additionally, addressing the hyperparameter dependencies of the hierarchical structure could further optimize the effectiveness and generalizability of the generator architecture in diverse application domains.

Conclusion

The authors present a compelling advancement in 3D generative modeling by leveraging hierarchical Gaussian splatting. Their approach stabilizes the training process and enhances rendering efficiency, with strong numerical results supporting their claims. The implications of this research are substantial for both the theoretical understanding and practical applications of 3D GANs, particularly in scenarios requiring efficient and high-fidelity image generations.