The ability to predict, anticipate and reason about future outcomes is a key component of
intelligent decision-making systems. In light of the success of deep learning in computer …

In a world with rapidly growing levels of automation, robotics is playing an increasingly
significant role in every aspect of human endeavour. In particular, many types of mobile …

We present a framework for video modeling based on denoising diffusion probabilistic
models that produces long-duration video completions in a variety of realistic environments …

Abstract From CNN, RNN, to ViT, we have witnessed remarkable advancements in video
prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated …

Denoising diffusion probabilistic models are a promising new class of generative models
that mark a milestone in high-quality image generation. This paper showcases their ability to …

The predictive learning of spatiotemporal sequences aims to generate future images by
learning from the historical context, where the visual dynamics are believed to have modular …

Video super-resolution (VSR) aims to restore a photo-realistic high-resolution (HR) video
frame from both its corresponding low-resolution (LR) frame (reference frame) and multiple …

We devise a cascade GAN approach to generate talking face video, which is robust to
different face shapes, view angles, facial characteristics, and noisy audio conditions. Instead …

Spatiotemporal predictive learning, though long considered to be a promising self-
supervised feature learning method, seldom shows its effectiveness beyond future video …

A video prediction model that generalizes to diverse scenes would enable intelligent agents
such as robots to perform a variety of tasks via planning with the model. However, while …