Can a user create a deep generative model by sketching a single example? Traditionally,
creating a GAN model has required the collection of a large-scale dataset of exemplars 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 …

We propose an interactive GAN-based sketch-to-image translation method that helps novice
users easily create images of simple objects. The user starts with a sparse sketch and a …

While the quality of GAN image synthesis has improved tremendously in recent years, our
ability to control and condition the output is still limited. Focusing on StyleGAN, we introduce …

This work presents an easy-to-use regularizer for GAN training, which helps explicitly link
some axes of the latent space to a set of pixels in the synthesized image. Establishing such …

Computer graphics has experienced a recent surge of data-centric approaches for
photorealistic and controllable content creation. StyleGAN in particular sets new standards …

Generative Adversarial Networks (GANs) have recently achieved impressive results for
many real-world applications, and many GAN variants have emerged with improvements in …

Metrics for evaluating generative models aim to measure the discrepancy between real and
generated images. The oftenused Frechet Inception Distance (FID) metric, for example …

We introduce SinGAN, an unconditional generative model that can be learned from a single
natural image. Our model is trained to capture the internal distribution of patches within the …

GANs can generate photo-realistic images from the domain of their training data. However,
those wanting to use them for creative purposes often want to generate imagery from a truly …