PStyle-3D: Example-Based 3-D-Aware Portrait Style Domain Adaptation.

Abstract

The creation of high-quality artistic portraits is a critical and desirable task in the field of computer vision. While recent3-D generative models have achieved impressive results in generating images with view consistency and intricate 3-D shapes, their application for generating artistic portraits is often more challenging than 2-D generative models due to the potentially destructive impact of 3-D structures on human faces. This article introduces a novel approach that leverages a meticulously designed domain feature extraction module to extract the specific feature information from both the source natural face domain and the target artistic portrait domain. These extracted features are seamlessly integrated into a 3-D representation, generating multiview consistent 3-D artistic portraits. To fuse the features of the source and target domains better, we propose a new module for domain adaptation. This module adds a path to the style path established by StyleGAN to introduce the artistic portrait domain information and regulate the target domain's feature information in $\mathcal {S}$ space. Our domain adaptation module is implemented in each StyleBlock of the 3-D representation generator to integrate the target domain information with the original facial information. Experimental results demonstrate that our approach generates high-quality 3-D artistic portraits that outperform existing approaches in preserving 3-D geometric information and multiview consistency.