“Transframer: Arbitrary Frame Prediction With Generative Models”, 2022-03-17 (; similar):
We present a general-purpose framework for image modeling 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.