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Portrait stylization is a long-standing task enabling extensive applications.Although 2D-based methods have made great progress in recent years, real-worldapplications such as metaverse and games often demand 3D content. On the otherhand, the requirement of 3D data, which is costly to acquire, significantlyimpedes the development of 3D portrait stylization methods. In this paper,inspired by the success of 3D-aware GANs that bridge 2D and 3D domains with 3Dfields as the intermediate representation for rendering 2D images, we propose anovel method, dubbed HyperStyle3D, based on 3D-aware GANs for 3D portraitstylization. At the core of our method is a hyper-network learned to manipulatethe parameters of the generator in a single forward pass. It not only offers astrong capacity to handle multiple styles with a single model, but also enablesflexible fine-grained stylization that affects only texture, shape, or localpart of the portrait. While the use of 3D-aware GANs bypasses the requirementof 3D data, we further alleviate the necessity of style images with the CLIPmodel being the stylization guidance. We conduct an extensive set ofexperiments across the style, attribute, and shape, and meanwhile, measure the3D consistency. These experiments demonstrate the superior capability of ourHyperStyle3D model in rendering 3D-consistent images in diverse styles,deforming the face shape, and editing various attributes.

HyperStyle3D: Text-Guided 3D Portrait Stylization via Hypernetworks