Generative Adversarial Networks (GANs) is a novel class of deep generative models that
has recently gained significant attention. GANs learn complex and high-dimensional …

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 …

Deep neural networks have attained great success in handling high dimensional data,
especially images. However, generating naturalistic images containing ginormous subjects …

With the rapid development of cartoon industry, various studies on two-dimensional (2D)
cartoon have been proposed for different application scenarios, such as quality assessment …

Formulated as a conditional generation problem, face animation aims at synthesizing
continuous face images from a single source image driven by a set of conditional face …

The CycleGAN framework allows for unsupervised image-to-image translation of unpaired
data. In a scenario of surgical training on a physical surgical simulator, this method can be …

Text summarization is a prominent task in natural language processing (NLP) that
condenses lengthy texts into concise summaries. Despite the success of existing supervised …

As a significant step for human face modeling editing and generation face landmarking aims
at extracting facial keypoints from images. A generalizable face landmarker is required in …

Converting a human portrait to anime style is a desirable but challenging problem. Existing
methods fail to resolve this problem due to the large inherent gap between two domains that …

Cartoon face generation is a task that aims to convert real-face photos to exquisite cartoons.
In this paper, we are interested in generating fine-grained cartoon faces for various groups …