  We present a general-purpose framework for image modelling and vision tasks
based on probabilistic frame prediction. Our approach unifies a broad range of
tasks, from image segmentation, to novel view synthesis and video
interpolation. We pair this framework with an architecture we term Transframer,
which uses U-Net and Transformer components to condition on annotated context
frames, and outputs sequences of sparse, compressed image features. Transframer
is the state-of-the-art on a variety of video generation benchmarks, is
competitive with the strongest models on few-shot view synthesis, and can
generate coherent 30 second videos from a single image without any explicit
geometric information. A single generalist Transframer simultaneously produces
promising results on 8 tasks, including semantic segmentation, image
classification and optical flow prediction with no task-specific architectural
components, demonstrating that multi-task computer vision can be tackled using
probabilistic image models. Our approach can in principle be applied to a wide
range of applications that require learning the conditional structure of
annotated image-formatted data.
