Synthesis and reconstruction of 3D human head has gained increasing interests
in computer vision and computer graphics recently. Existing state-of-the-art 3D
generative adversarial networks (GANs) for 3D human head synthesis are either
limited to near-frontal views or hard to preserve 3D consistency in large view
angles. We propose PanoHead, the first 3D-aware generative model that enables
high-quality view-consistent image synthesis of full heads in $360^\circ$ with
diverse appearance and detailed geometry using only in-the-wild unstructured
images for training. At its core, we lift up the representation power of recent
3D GANs and bridge the data alignment gap when training from in-the-wild images
with widely distributed views. Specifically, we propose a novel two-stage
self-adaptive image alignment for robust 3D GAN training. We further introduce
a tri-grid neural volume representation that effectively addresses front-face
and back-head feature entanglement rooted in the widely-adopted tri-plane
formulation. Our method instills prior knowledge of 2D image segmentation in
adversarial learning of 3D neural scene structures, enabling compositable head
synthesis in diverse backgrounds. Benefiting from these designs, our method
significantly outperforms previous 3D GANs, generating high-quality 3D heads
with accurate geometry and diverse appearances, even with long wavy and afro
hairstyles, renderable from arbitrary poses. Furthermore, we show that our
system can reconstruct full 3D heads from single input images for personalized
realistic 3D avatars.

提出了 PanoHead，这是第一种具有 3D 感知能力的生成模型，可以通过采用野外非结构图像进行训练来生成全头的高质量视角一致的图像，其核心是通过 3D GANs 来提高表示的能力并解决数据对齐差距。该方法可以在任意姿势下呈现准确几何形态和多样外观的高质量 3D 头部。