Animating a still image offers an engaging visual experience. Traditional image animation
techniques mainly focus on animating natural scenes with stochastic dynamics (eg clouds …

Generative Adversarial Networks (GANs) have achieved remarkable achievements in image
synthesis. These successes of GANs rely on large scale datasets, requiring too much cost …

Denoising diffusion models (DDMs) have led to staggering performance leaps in image
generation, editing and restoration. However, existing DDMs use very large datasets for …

This review focuses on recent advancements in data-driven methods for analyzing flow and
transport in porous media, which are showing promising potential for applications in energy …

Despite recent advances in image-to-video generation, better controllability and local
animation are less explored. Most existing image-to-video methods are not locally aware …

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 …

Diffusion models exhibited tremendous progress in image and video generation, exceeding
GANs in quality and diversity. However, they are usually trained on very large datasets and …

We present GANimator, a generative model that learns to synthesize novel motions from a
single, short motion sequence. GANimator generates motions that resemble the core …

We present SinDiffusion, leveraging denoising diffusion models to capture internal
distribution of patches from a single natural image. SinDiffusion significantly improves the …

Image super-resolution (SR) methods can generate remote sensing images with high spatial
resolution without increasing the cost of acquisition equipment, thereby providing a feasible …