Video Occupancy Models
<|reference_start|>Video Occupancy Models: We introduce a new family of video prediction models designed to support downstream control tasks. We call these models Video Occupancy models (VOCs). VOCs operate in a compact latent space, thus avoiding the need to make predictions about individual pixels. Unlike prior latent-space world models, VOCs directly predict the discounted distribution of future states in a single step, thus avoiding the need for multistep roll-outs. We show that both properties are beneficial when building predictive models of video for use in downstream control. Code is available at \href{https://github.com/manantomar/video-occupancy-models}{\texttt{github.com/manantomar/video-occupancy-models}}.<|reference_end|>
@article{tomar2024video,
    title={Video Occupancy Models},
    author={Manan Tomar, Philippe Hansen-Estruch, Philip Bachman, Alex Lamb, John
  Langford, Matthew E. Taylor, Sergey Levine},
    journal={arXiv preprint arXiv:2407.09533},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09533},
    primaryClass={cs.CV cs.AI}
}