  3D-aware face generators are typically trained on 2D real-life face image
datasets that primarily consist of near-frontal face data, and as such, they
are unable to construct one-quarter headshot 3D portraits with complete head,
neck, and shoulder geometry. Two reasons account for this issue: First,
existing facial recognition methods struggle with extracting facial data
captured from large camera angles or back views. Second, it is challenging to
learn a distribution of 3D portraits covering the one-quarter headshot region
from single-view data due to significant geometric deformation caused by
diverse body poses. To this end, we first create the dataset
360{\deg}-Portrait-HQ (360{\deg}PHQ for short) which consists of high-quality
single-view real portraits annotated with a variety of camera parameters (the
yaw angles span the entire 360{\deg} range) and body poses. We then propose
3DPortraitGAN, the first 3D-aware one-quarter headshot portrait generator that
learns a canonical 3D avatar distribution from the 360{\deg}PHQ dataset with
body pose self-learning. Our model can generate view-consistent portrait images
from all camera angles with a canonical one-quarter headshot 3D representation.
Our experiments show that the proposed framework can accurately predict
portrait body poses and generate view-consistent, realistic portrait images
with complete geometry from all camera angles.
