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While substantial progresses have been made in automated 2D portraitstylization, admirable 3D portrait stylization from a single user photo remainsto be an unresolved challenge. One primary obstacle here is the lack of highquality stylized 3D training data. In this paper, we propose a novel framework\emph{AgileGAN3D} that can produce 3D artistically appealing and personalizedportraits with detailed geometry. New stylization can be obtained with just afew (around 20) unpaired 2D exemplars. We achieve this by first leveragingexisting 2D stylization capabilities, \emph{style prior creation}, to produce alarge amount of augmented 2D style exemplars. These augmented exemplars aregenerated with accurate camera pose labels, as well as paired real face images,which prove to be critical for the downstream 3D stylization task. Capitalizingon the recent advancement of 3D-aware GAN models, we perform \emph{guidedtransfer learning} on a pretrained 3D GAN generator to producemulti-view-consistent stylized renderings. In order to achieve 3D GAN inversionthat can preserve subject's identity well, we incorporate \emph{multi-viewconsistency loss} in the training of our encoder. Our pipeline demonstratesstrong capability in turning user photos into a diverse range of 3D artisticportraits. Both qualitative results and quantitative evaluations have beenconducted to show the superior performance of our method. Code and pretrainedmodels will be released for reproduction purpose.

AgileGAN3D: Few-Shot 3D Portrait Stylization by Augmented Transfer Learning