Image-based virtual try-on aims to synthesize an image of a person wearing a given clothing
item. To solve the task, the existing methods warp the clothing item to fit the person's body …

We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large
scale image generation. To this end, we scale and enhance the autoregressive priors used …

In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which
allows attention-driven, long-range dependency modeling for image generation tasks …

The proposed method, Discriminator Guidance, aims to improve sample generation of pre-
trained diffusion models. The approach introduces a discriminator that gives explicit …

Abstract Conditional Generative Adversarial Networks (cGANs) have enabled controllable
image synthesis for many computer vision and graphics applications. However, recent …

Generative adversarial networks (GANs) have shown outstanding performance on a wide
range of problems in computer vision, graphics, and machine learning, but often require …

Compressed sensing (CS) provides an elegant framework for recovering sparse signals
from compressed measurements. For example, CS can exploit the structure of natural …

Our work focuses on addressing sample deficiency from low-density regions of data
manifold in common image datasets. We leverage diffusion process based generative …

Posterior inference with an intractable likelihood is becoming an increasingly common task
in scientific domains which rely on sophisticated computer simulations. Typically, these …

In the decade, researchers have proposed many remarkable algorithms in structural design,
training modes, etc., in the field of Generative AI. However, with the explosive growth of …