  The ability to predict future visual observations conditioned on past
observations and motor commands can enable embodied agents to plan solutions to
a variety of tasks in complex environments. This work shows that we can create
good video prediction models by pre-training transformers via masked visual
modeling. Our approach, named MaskViT, is based on two simple design decisions.
First, for memory and training efficiency, we use two types of window
attention: spatial and spatiotemporal. Second, during training, we mask a
variable percentage of tokens instead of a fixed mask ratio. For inference,
MaskViT generates all tokens via iterative refinement where we incrementally
decrease the masking ratio following a mask scheduling function. On several
datasets we demonstrate that MaskViT outperforms prior works in video
prediction, is parameter efficient, and can generate high-resolution videos
(256x256). Further, we demonstrate the benefits of inference speedup (up to
512x) due to iterative decoding by using MaskViT for planning on a real robot.
Our work suggests that we can endow embodied agents with powerful predictive
models by leveraging the general framework of masked visual modeling with
minimal domain knowledge.
