  Self-supervised learning of image representations by predicting future frames
is a promising direction but still remains a challenge. This is because of the
under-determined nature of frame prediction; multiple potential futures can
arise from a single current frame. To tackle this challenge, in this paper, we
revisit the idea of stochastic video generation that learns to capture
uncertainty in frame prediction and explore its effectiveness for
representation learning. Specifically, we design a framework that trains a
stochastic frame prediction model to learn temporal information between frames.
Moreover, to learn dense information within each frame, we introduce an
auxiliary masked image modeling objective along with a shared decoder
architecture. We find this architecture allows for combining both objectives in
a synergistic and compute-efficient manner. We demonstrate the effectiveness of
our framework on a variety of tasks from video label propagation and
vision-based robot learning domains, such as video segmentation, pose tracking,
vision-based robotic locomotion, and manipulation tasks. Code is available on
the project webpage: https://sites.google.com/view/2024rsp.
