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InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

We present a method for learning to generate unbounded flythrough videos of natural scenes starting from a single view. 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 render virtual camera trajectories, including cyclic camera paths, allowing our model to learn stable view generation from a collection of single views. At test time, despite never having seen a video, 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. Our project webpage, including video results, is at https://infinite-nature-zero.github.io.

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Authors and Affiliations

  1. Google Research, Mountain View, USA

    Zhengqi Li, Qianqian Wang & Noah Snavely

  2. Cornell Tech, Cornell University, Ithaca, USA

    Qianqian Wang

  3. UC Berkeley, Berkeley, USA

    Angjoo Kanazawa

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  1. Zhengqi Li
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  2. Qianqian Wang
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  3. Noah Snavely
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  4. Angjoo Kanazawa
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Correspondence to Zhengqi Li .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Li, Z., Wang, Q., Snavely, N., Kanazawa, A. (2022). InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13661. Springer, Cham. https://doi.org/10.1007/978-3-031-19769-7_30

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