  Transformers have been successful for many natural language processing tasks.
However, applying transformers to the video domain for tasks such as long-term
video generation and scene understanding has remained elusive due to the high
computational complexity and the lack of natural tokenization. In this paper,
we propose the Object-Centric Video Transformer (OCVT) which utilizes an
object-centric approach for decomposing scenes into tokens suitable for use in
a generative video transformer. By factoring the video into objects, our fully
unsupervised model is able to learn complex spatio-temporal dynamics of
multiple interacting objects in a scene and generate future frames of the
video. Our model is also significantly more memory-efficient than pixel-based
models and thus able to train on videos of length up to 70 frames with a single
48GB GPU. We compare our model with previous RNN-based approaches as well as
other possible video transformer baselines. We demonstrate OCVT performs well
when compared to baselines in generating future frames. OCVT also develops
useful representations for video reasoning, achieving start-of-the-art
performance on the CATER task.
