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.

翻译：我们提出了一种面向对象视频预测任务的新型框架，即提取视频序列的组成结构，同时从视觉观测中建模物体动力学与交互关系，以预测未来物体状态，并据此生成后续视频帧。为实现有意义的时空物体表征学习与精准的物体状态预测，我们提出了两种新型面向对象视频预测器（OCVP）Transformer模块，通过解耦时间动态过程与物体交互机制，显著提升了预测性能。实验表明，基于OCVP预测器的面向对象预测框架在两个不同数据集上均优于物体无关的视频预测模型，同时保持了物体表征的一致性与准确性。