Transframer: Arbitrary Frame Prediction with Generative Models

• URL: http://arxiv.org/abs/2203.09494v2
• Date: Fri, 18 Mar 2022 10:34:43 GMT
• Title: Transframer: Arbitrary Frame Prediction with Generative Models
• Authors: Charlie Nash, Jo\~ao Carreira, Jacob Walker, Iain Barr, Andrew Jaegle, Mateusz Malinowski, Peter Battaglia
• Abstract summary: We present a general-purpose framework for image modelling and vision tasks based on probabilistic frame prediction. We pair this framework with an architecture we term Transframer, which uses U-Net and Transformer components to condition on annotated context frames.
• Score: 21.322137081404904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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