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- W4288043105 abstract "We present a method for learning to generate unbounded flythrough videos of natural scenes starting from a single view, where this capability is learned from a collection of single photographs, without requiring camera poses or even multiple views of each scene. To achieve this, we propose a novel self-supervised view generation training paradigm, where we sample and rendering virtual camera trajectories, including cyclic ones, allowing our model to learn stable view generation from a collection of single views. At test time, despite never seeing a video during training, our approach can take a single image and generate long camera trajectories comprised of hundreds of new views with realistic and diverse content. We compare our approach with recent state-of-the-art supervised view generation methods that require posed multi-view videos and demonstrate superior performance and synthesis quality." @default.
- W4288043105 created "2022-07-27" @default.
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- W4288043105 date "2022-07-22" @default.
- W4288043105 modified "2023-10-17" @default.
- W4288043105 title "InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images" @default.
- W4288043105 doi "https://doi.org/10.48550/arxiv.2207.11148" @default.
- W4288043105 hasPublicationYear "2022" @default.
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