  In contrast to the traditional avatar creation pipeline which is a costly
process, contemporary generative approaches directly learn the data
distribution from photographs. While plenty of works extend unconditional
generative models and achieve some levels of controllability, it is still
challenging to ensure multi-view consistency, especially in large poses. In
this work, we propose a network that generates 3D-aware portraits while being
controllable according to semantic parameters regarding pose, identity,
expression and illumination. Our network uses neural scene representation to
model 3D-aware portraits, whose generation is guided by a parametric face model
that supports explicit control. While the latent disentanglement can be further
enhanced by contrasting images with partially different attributes, there still
exists noticeable inconsistency in non-face areas, e.g., hair and background,
when animating expressions. Wesolve this by proposing a volume blending
strategy in which we form a composite output by blending dynamic and static
areas, with two parts segmented from the jointly learned semantic field. Our
method outperforms prior arts in extensive experiments, producing realistic
portraits with vivid expression in natural lighting when viewed from free
viewpoints. It also demonstrates generalization ability to real images as well
as out-of-domain data, showing great promise in real applications.
