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 naïve 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.
大多数3D生成对抗网络（3D GANs）的进步主要依赖于基于光线投射的体积渲染，这导致了巨大的渲染成本。一种有前景的替代方法是基于光栅化的3D高斯涂抹（3D-GS），提供了更快的渲染速度和明确的3D表达。在本文中，我们通过利用其高效和明确的特性，探索高斯作为3D GANs的3D表示。然而，在对抗性框架中，我们观察到，一个简单的生成器架构存在训练不稳定性，并且缺乏调整高斯尺度的能力。这导致了模型发散和由于缺乏对高斯初始化位置的适当指导以及适应性地管理其尺度的密集化而产生的视觉伪像。为了解决这些问题，我们引入了一个具有层次化多尺度高斯表示的生成器架构，有效地规范了生成高斯的位置和尺度。具体来说，我们设计了一个高斯层次结构，其中更细层次的高斯由其粗糙层次的对应物参数化；更细层次的高斯位置将位于其粗糙层次对应物附近，且尺度随着层次的细化而单调减小，从而模拟3D场景的粗糙和细致细节。实验结果表明，与具有可比3D生成能力的最先进的3D一致性GANs相比，我们的方法实现了显著更快的渲染速度（提高100倍）。