Modern 3D-GANs synthesize geometry and texture by training on large-scale datasets with
a consistent structure. Training such models on stylized, artistic data, with often unknown …

Shape and geometric patterns are essential in defining stylistic identity. However current 3D
style transfer methods predominantly focus on transferring colors and textures often …

Image and shape editing are ubiquitous among digital artworks. Graphics algorithms
facilitate artists and designers to achieve desired editing intents without going through …

Portrait stylization is a long-standing task enabling extensive applications. Although 2D-
based methods have made great progress in recent years, real-world applications such as …

We propose an approach for interactive 3D face editing based on deep generative models.
Most of the current face modeling methods rely on linear methods and cannot express …

Recent advances in 3D face stylization have made significant strides in few to zero-shot
settings. However the degree of stylization achieved by existing methods is often not …

Generative neural radiance fields (NeRF) bring image generation into the 3D era, which
have delivered impressive generation quality and 3D consistency, especially in the face …

Recreating historical portraits with accuracy and artistic diversity has always been a
challenge in the field of computer vision. To ensure faithful reinvention of portrait images, it is …

Recent advances in generative models enable high-quality facial image stylization. Toonify
is a popular StyleGAN-based framework that has been widely used for facial image …

Abstract 2D caricature editing has shown superior performance. However, 3D exaggerated
caricature face (ECF) modeling with flexible shape and texture editing capabilities is far from …