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 …

The performance of video prediction has been greatly boosted by advanced deep neural
networks. However, most of the current methods suffer from large model sizes and require …

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 …

Controllable video generation has gained significant attention in recent years. However, two
main limitations persist: Firstly, most existing works focus on either text, image, or trajectory …

Recent approaches to point tracking are able to recover the trajectory of any scene point
through a large portion of a video despite the presence of occlusions. They are however too …

Our work addresses long-term motion context issues for predicting future frames. To predict
the future precisely, it is required to capture which long-term motion context (eg, walking or …

Designing video prediction models that account for the inherent uncertainty of the future is
challenging. Most works in the literature are based on stochastic image-autoregressive …

Generating controllable videos conforming to user intentions is an appealing yet challenging
topic in computer vision. To enable maneuverable control in line with user intentions, a …

This presentation introduces a self-supervised learning approach to the synthesis of new
videos clips from old ones, with several new key elements for improved spatial resolution …

In this paper, we investigate the challenge of spatio-temporal video prediction, which
involves generating future videos based on historical data streams. Existing approaches …