Learning 3D head priors from large 2D image collections is an important step
towards high-quality 3D-aware human modeling. A core requirement is an
efficient architecture that scales well to large-scale datasets and large image
resolutions. Unfortunately, existing 3D GANs struggle to scale to generate
samples at high resolutions due to their relatively slow train and render
speeds, and typically have to rely on 2D superresolution networks at the
expense of global 3D consistency. To address these challenges, we propose
Generative Gaussian Heads (GGHead), which adopts the recent 3D Gaussian
Splatting representation within a 3D GAN framework. To generate a 3D
representation, we employ a powerful 2D CNN generator to predict Gaussian
attributes in the UV space of a template head mesh. This way, GGHead exploits
the regularity of the template's UV layout, substantially facilitating the
challenging task of predicting an unstructured set of 3D Gaussians. We further
improve the geometric fidelity of the generated 3D representations with a novel
total variation loss on rendered UV coordinates. Intuitively, this
regularization encourages that neighboring rendered pixels should stem from
neighboring Gaussians in the template's UV space. Taken together, our pipeline
can efficiently generate 3D heads trained only from single-view 2D image
observations. Our proposed framework matches the quality of existing 3D head
GANs on FFHQ while being both substantially faster and fully 3D consistent. As
a result, we demonstrate real-time generation and rendering of high-quality
3D-consistent heads at $1024^2$ resolution for the first time.

从大型 2D 图像集合中学习 3D 头部先验是实现高质量 3D 意识人体建模的重要步骤。本研究提出了一种有效的架构，名为 Generative Gaussian Heads（GGHead），其采用了最近的 3D 高斯光斑表示法，并结合了 2D 图像生成器和全变差损失，能够快速生成并渲染具有高分辨率和全局 3D 一致性的头部实例。

GGHead：快速且具有普适性的三维高斯头部

GGHead: Fast and Generalizable 3D Gaussian Heads

In this paper, we study Text-to-3D content generation leveraging 2D diffusion
priors to enhance the quality and detail of the generated 3D models. Recent
progress (Magic3D) in text-to-3D has shown that employing high-resolution
(e.g., 512 x 512) renderings can lead to the production of high-quality 3D
models using latent diffusion priors. To enable rendering at even higher
resolutions, which has the potential to further augment the quality and detail
of the models, we propose a novel approach that combines multiple noise
estimation processes with a pretrained 2D diffusion prior. Distinct from the
Bar-Tal et al.s' study which binds multiple denoised results to generate images
from texts, our approach integrates the computation of scoring distillation
losses such as SDS loss and VSD loss which are essential techniques for the 3D
content generation with 2D diffusion priors. We experimentally evaluated the
proposed approach. The results show that the proposed approach can generate
high-quality details compared to the baselines.

通过利用 2D 扩散先验提高文本到 3D 内容生成的质量和细节，本文提出了一种结合多个噪声估计过程和预训练的 2D 扩散先验的新方法，实验证明该方法相较于基线能够生成更高质量的细节。