The predictive learning of spatiotemporal sequences aims to generate future images by
learning from the historical frames, where spatial appearances and temporal variations are …

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 …

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

We present PredRNN++, a recurrent network for spatiotemporal predictive learning. In
pursuit of a great modeling capability for short-term video dynamics, we make our network …

This work introduces double-mapping Gated Recurrent Units (dGRU), an extension of
standard GRUs where the input is considered as a recurrent state. An extra set of logic gates …

Learning from spatio-temporal data has numerous applications such as human-behavior
analysis, object tracking, video compression, and physics simulation. However, existing …

Predicting future frames in videos has become a promising direction of research for both
computer vision and robot learning communities. The core of this problem involves moving …

Video prediction is a challenging computer vision task that has a wide range of applications.
In this work, we present a new family of Transformer-based models for video prediction …

Recent years have witnessed remarkable advances in spatiotemporal predictive learning,
incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training …

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