  Stylized 3D avatars have become increasingly prominent in our modern life.
Creating these avatars manually usually involves laborious selection and
adjustment of continuous and discrete parameters and is time-consuming for
average users. Self-supervised approaches to automatically create 3D avatars
from user selfies promise high quality with little annotation cost but fall
short in application to stylized avatars due to a large style domain gap. We
propose a novel self-supervised learning framework to create high-quality
stylized 3D avatars with a mix of continuous and discrete parameters. Our
cascaded domain bridging framework first leverages a modified portrait
stylization approach to translate input selfies into stylized avatar renderings
as the targets for desired 3D avatars. Next, we find the best parameters of the
avatars to match the stylized avatar renderings through a differentiable
imitator we train to mimic the avatar graphics engine. To ensure we can
effectively optimize the discrete parameters, we adopt a cascaded
relaxation-and-search pipeline. We use a human preference study to evaluate how
well our method preserves user identity compared to previous work as well as
manual creation. Our results achieve much higher preference scores than
previous work and close to those of manual creation. We also provide an
ablation study to justify the design choices in our pipeline.
