The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks
(CNNs) in creating artistic imagery by separating and recombining image content and style …

Generative Adversarial Networks (GANs) have achieved impressive results in various image
synthesis tasks, and are becoming a hot topic in computer vision research because of the …

High-quality, diverse, and photorealistic images can now be generated by unconditional
GANs (eg, StyleGAN). However, limited options exist to control the generation process using …

Recent studies on StyleGAN show high performance on artistic portrait generation by
transfer learning with limited data. In this paper, we explore more challenging exemplar …

Generating high-quality artistic portrait videos is an important and desirable task in computer
graphics and vision. Although a series of successful portrait image toonification models built …

In this paper, we address the challenging problem of 3D toonification, which involves
transferring the style of an artistic domain onto a target 3D face with stylized geometry and …

Deep generative models have reduced the technical barriers to creating visual content. They
can free a person from the need to develop all the skills to create the fine details of a realistic …

In this paper, we propose a novel framework to translate a portrait photo-face into an anime
appearance. Different from existing translation methods which do not designate specific …

Abstract Generative Adversarial Networks (GANs) have made a dramatic leap in high-fidelity
image synthesis and stylized face generation. Recently, a layer-swap** mechanism has …

Unsupervised image-to-image translation aims at learning a map** between two visual
domains. However, learning a translation across large geometry variations al-ways ends up …