Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

In the deep learning era, long video generation of high-quality still remains challenging due
to the spatio-temporal complexity and continuity of videos. Existing prior works have …

Remarkable achievements have been attained with Generative Adversarial Networks
(GANs) in image-to-image translation. However, due to a tremendous amount of parameters …

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

We present a method for learning to generate unbounded flythrough videos of natural
scenes starting from a single view. This capability is learned from a collection of single …

In recent years, many deep learning based image inpainting methods have been developed
by the research community. Some of those methods have shown impressive image …

Despite increasingly realistic image quality, recent 3D image generative models often
operate on 3D volumes of fixed extent with limited camera motions. We investigate the task …

During image editing, existing deep generative models tend to re-synthesize the entire
output from scratch, including the unedited regions. This leads to a significant waste of …

Exceptional text-to-image (T2I) generation results of Stable Diffusion models (SDMs) come
with substantial computational demands. To resolve this issue, recent research on efficient …