Previous animatable 3D-aware GANs for human generation have primarily focused
on either the human head or full body. However, head-only videos are relatively
uncommon in real life, and full body generation typically does not deal with
facial expression control and still has challenges in generating high-quality
results. Towards applicable video avatars, we present an animatable 3D-aware
GAN that generates portrait images with controllable facial expression, head
pose, and shoulder movements. It is a generative model trained on unstructured
2D image collections without using 3D or video data. For the new task, we base
our method on the generative radiance manifold representation and equip it with
learnable facial and head-shoulder deformations. A dual-camera rendering and
adversarial learning scheme is proposed to improve the quality of the generated
faces, which is critical for portrait images. A pose deformation processing
network is developed to generate plausible deformations for challenging regions
such as long hair. Experiments show that our method, trained on unstructured 2D
images, can generate diverse and high-quality 3D portraits with desired control
over different properties.

基于非结构化的 2D 图像集合，我们提出了一种可调节面部表情、头部姿势和肩膀运动的三维感知生成对抗网络，该网络能够生成多样且高质量的三维头像。

AniPortraitGAN：从 2D 图像集合生成可动态调整的 3D 肖像

AniPortraitGAN: Animatable 3D Portrait Generation from 2D Image  Collections

We present a caricature generation framework based on shape and style
manipulation using StyleGAN. Our framework, dubbed StyleCariGAN, automatically
creates a realistic and detailed caricature from an input photo with optional
controls on shape exaggeration degree and color stylization type. The key
component of our method is shape exaggeration blocks that are used for
modulating coarse layer feature maps of StyleGAN to produce desirable
caricature shape exaggerations. We first build a layer-mixed StyleGAN for
photo-to-caricature style conversion by swapping fine layers of the StyleGAN
for photos to the corresponding layers of the StyleGAN trained to generate
caricatures. Given an input photo, the layer-mixed model produces detailed
color stylization for a caricature but without shape exaggerations. We then
append shape exaggeration blocks to the coarse layers of the layer-mixed model
and train the blocks to create shape exaggerations while preserving the
characteristic appearances of the input. Experimental results show that our
StyleCariGAN generates realistic and detailed caricatures compared to the
current state-of-the-art methods. We demonstrate StyleCariGAN also supports
other StyleGAN-based image manipulations, such as facial expression control.

通过 StyleGAN，我们提出了一个基于形状和风格调整的讽刺画生成框架，名为 StyleCariGAN，它可以从输入照片自动生成真实而详细的讽刺画，并支持形状夸张度和颜色风格化类型的可选控制。我们的关键方法是形状夸张模块，它用于调制 StyleGAN 的粗糙层特征图以产生所需的讽刺画形状夸张。我们的实验结果表明，相比当前最先进的方法，StyleCariGAN 生成的讽刺画更加真实和详细，并且还支持其他基于 StyleGAN 的图像操作，如面部表情控制。