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

While progress in 2D generative models of human appearance has been rapid,
many applications require 3D avatars that can be animated and rendered.
Unfortunately, most existing methods for learning generative models of 3D
humans with diverse shape and appearance require 3D training data, which is
limited and expensive to acquire. The key to progress is hence to learn
generative models of 3D avatars from abundant unstructured 2D image
collections. However, learning realistic and complete 3D appearance and
geometry in this under-constrained setting remains challenging, especially in
the presence of loose clothing such as dresses. In this paper, we propose a new
adversarial generative model of realistic 3D people from 2D images. Our method
captures shape and deformation of the body and loose clothing by adopting a
holistic 3D generator and integrating an efficient and flexible articulation
module. To improve realism, we train our model using multiple discriminators
while also integrating geometric cues in the form of predicted 2D normal maps.
We experimentally find that our method outperforms previous 3D- and
articulation-aware methods in terms of geometry and appearance. We validate the
effectiveness of our model and the importance of each component via systematic
ablation studies.

本文提出了一种从 2D 图像生成逼真的 3D 人物的新方法，并使用多个辨别器和 2D 法线图形式的几何线索来训练模型，取得了在几何和外观方面超过以前 3D 和关节感知方法的性能，通过系统的消融研究验证了模型的有效性和每个组件的重要性。