Deep generative models have been an upsurge in the deep learning community since they
were proposed. These models are designed for generating new synthetic data including …

Supervised image-to-image translation has been proven to generate realistic images with
sharp details and to have good quantitative performance. Such methods are trained on a …

Image-to-image translation (I2I) aims to transfer images from a source domain to a target
domain while preserving the content representations. I2I has drawn increasing attention and …

We present a method for semantically transferring the visual appearance of one natural
image to another. Specifically, our goal is to generate an image in which objects in a source …

Although existing artistic style transfer methods have achieved significant improvement with
deep neural networks, they still suffer from artifacts such as disharmonious colors and …

Recent advancements in text-to-image generative models have demonstrated a remarkable
ability to capture a deep semantic understanding of images. In this work, we leverage this …

Recent advances in deep learning have shown exciting promise in filling large holes in
natural images with semantically plausible and context aware details, impacting …

Large-scale text-to-image models pre-trained on massive text-image pairs show excellent
performance in image synthesis recently. However, image can provide more intuitive visual …

The generative adversarial network (GAN) framework has emerged as a powerful tool for
various image and video synthesis tasks, allowing the synthesis of visual content in an …

Synthesizing realistic animations of humans, animals, and even imaginary creatures, has
long been a goal for artists and computer graphics professionals. Compared to the imaging …