While recent advances in 3D-aware Generative Adversarial Networks (GANs) have
aided the development of near-frontal view human face synthesis, the challenge
of comprehensively synthesizing a full 3D head viewable from all angles still
persists. Although PanoHead proves the possibilities of using a large-scale
dataset with images of both frontal and back views for full-head synthesis, it
often causes artifacts for back views. Based on our in-depth analysis, we found
the reasons are mainly twofold. First, from network architecture perspective,
we found each plane in the utilized tri-plane/tri-grid representation space
tends to confuse the features from both sides, causing "mirroring" artifacts
(e.g., the glasses appear in the back). Second, from data supervision aspect,
we found that existing discriminator training in 3D GANs mainly focuses on the
quality of the rendered image itself, and does not care much about its
plausibility with the perspective from which it was rendered. This makes it
possible to generate "face" in non-frontal views, due to its easiness to fool
the discriminator. In response, we propose SphereHead, a novel tri-plane
representation in the spherical coordinate system that fits the human head's
geometric characteristics and efficiently mitigates many of the generated
artifacts. We further introduce a view-image consistency loss for the
discriminator to emphasize the correspondence of the camera parameters and the
images. The combination of these efforts results in visually superior outcomes
with significantly fewer artifacts. Our code and dataset are publicly available
at this https URL

通过使用球面坐标系中的新型三平面表示，以及引入视图 - 图像一致性损失来减少生成的伪影，该研究提出了一种解决全方位，全角度三维头部合成挑战的方法。

SphereHead: 基于球面三平面表示的稳定三维全头合成

SphereHead: Stable 3D Full-head Synthesis with Spherical Tri-plane  Representation

3D-aware Generative Adversarial Networks (GANs) have shown remarkable
progress in learning to generate multi-view-consistent images and 3D geometries
of scenes from collections of 2D images via neural volume rendering. Yet, the
significant memory and computational costs of dense sampling in volume
rendering have forced 3D GANs to adopt patch-based training or employ
low-resolution rendering with post-processing 2D super resolution, which
sacrifices multiview consistency and the quality of resolved geometry.
Consequently, 3D GANs have not yet been able to fully resolve the rich 3D
geometry present in 2D images. In this work, we propose techniques to scale
neural volume rendering to the much higher resolution of native 2D images,
thereby resolving fine-grained 3D geometry with unprecedented detail. Our
approach employs learning-based samplers for accelerating neural rendering for
3D GAN training using up to 5 times fewer depth samples. This enables us to
explicitly "render every pixel" of the full-resolution image during training
and inference without post-processing superresolution in 2D. Together with our
strategy to learn high-quality surface geometry, our method synthesizes
high-resolution 3D geometry and strictly view-consistent images while
maintaining image quality on par with baselines relying on post-processing
super resolution. We demonstrate state-of-the-art 3D gemetric quality on FFHQ
and AFHQ, setting a new standard for unsupervised learning of 3D shapes in 3D
GANs.

本研究提出了一种扩展神经体素渲染至高分辨率 2D 图像的技术，使得生成对抗网络（GAN）能够以前所未有的细节分辨率合成高分辨率的三维几何物体，并且在维持图像质量的同时保持严格的视角一致性，从而树立了无监督学习 3D GAN 中的三维形状的新标准。

你所看到的就是你 GAN: 用于高保真 3D GAN 中几何渲染的每个像素

What You See is What You GAN: Rendering Every Pixel for High-Fidelity  Geometry in 3D GANs

3D-aware generative adversarial networks (GANs) synthesize high-fidelity and
multi-view-consistent facial images using only collections of single-view 2D
imagery. Towards fine-grained control over facial attributes, recent efforts
incorporate 3D Morphable Face Model (3DMM) to describe deformation in
generative radiance fields either explicitly or implicitly. Explicit methods
provide fine-grained expression control but cannot handle topological changes
caused by hair and accessories, while implicit ones can model varied topologies
but have limited generalization caused by the unconstrained deformation fields.
We propose a novel 3D GAN framework for unsupervised learning of generative,
high-quality and 3D-consistent facial avatars from unstructured 2D images. To
achieve both deformation accuracy and topological flexibility, we propose a 3D
representation called Generative Texture-Rasterized Tri-planes. The proposed
representation learns Generative Neural Textures on top of parametric mesh
templates and then projects them into three orthogonal-viewed feature planes
through rasterization, forming a tri-plane feature representation for volume
rendering. In this way, we combine both fine-grained expression control of
mesh-guided explicit deformation and the flexibility of implicit volumetric
representation. We further propose specific modules for modeling mouth interior
which is not taken into account by 3DMM. Our method demonstrates
state-of-the-art 3D-aware synthesis quality and animation ability through
extensive experiments. Furthermore, serving as 3D prior, our animatable 3D
representation boosts multiple applications including one-shot facial avatars
and 3D-aware stylization.

本文提出了一种使用 3D GAN 生成高保真的、多视角一致的面部图像的方法，可以通过不受限制的变形场实现细粒度的表情控制和拓扑灵活性，并在口腔内部方面进行特定模块的建模来提高动画能力，这种 3D 表示还可以作为 3D 先验对于多种应用具有提升作用。