  We propose a novel framework for the task of object-centric video prediction,
i.e., extracting the compositional structure of a video sequence, as well as
modeling objects dynamics and interactions from visual observations in order to
predict the future object states, from which we can then generate subsequent
video frames. With the goal of learning meaningful spatio-temporal object
representations and accurately forecasting object states, we propose two novel
object-centric video predictor (OCVP) transformer modules, which decouple the
processing of temporal dynamics and object interactions, thus presenting an
improved prediction performance. In our experiments, we show how our
object-centric prediction framework utilizing our OCVP predictors outperforms
object-agnostic video prediction models on two different datasets, while
maintaining consistent and accurate object representations.

Computer Vision and Pattern Recognition