33D-aware face generators are commonly trained on 2D real-life face image
datasets. Nevertheless, existing facial recognition methods often struggle to
extract face data captured from various camera angles. Furthermore, in-the-wild
images with diverse body poses introduce a high-dimensional challenge for
3D-aware generators, making it difficult to utilize data that contains complete
neck and shoulder regions. Consequently, these face image datasets often
contain only near-frontal face data, which poses challenges for 3D-aware face
generators to construct \textit{full-head} 3D portraits. To this end, we first
create the dataset {$\it{360}^{\circ}$}-\textit{Portrait}-\textit{HQ}
(\textit{$\it{360}^{\circ}$PHQ}), which consists of high-quality single-view
real portraits annotated with a variety of camera parameters {(the yaw angles
span the entire $360^{\circ}$ range)} and body poses. We then propose
\textit{3DPortraitGAN}, the first 3D-aware full-head portrait generator that
learns a canonical 3D avatar distribution from the body-pose-various
\textit{$\it{360}^{\circ}$PHQ} dataset with body pose self-learning. Our model
can generate view-consistent portrait images from all camera angles
(${360}^{\circ}$) with a full-head 3D representation. We incorporate a
mesh-guided deformation field into volumetric rendering to produce deformed
results to generate portrait images that conform to the body pose distribution
of the dataset using our canonical generator. We integrate two pose predictors
into our framework to predict more accurate body poses to address the issue of
inaccurately estimated body poses in our dataset. Our experiments show that the
proposed framework can generate view-consistent, realistic portrait images with
complete geometry from all camera angles and accurately predict portrait body
pose.

我们提供了第一个 3D 感知的全头像肖像生成器，它从包含多种摄像参数和身体姿势的高质量单视角真实肖像数据集学习一个规范化的 3D 化身分布，并利用我们的规范化生成器产生符合数据集身体姿态分布的变形结果来生成自各个摄像角度（360°）具有完整 3D 几何的视一致逼真肖像图像。