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Diverse Generation from a Single Video Made Possible

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

GANs are able to perform generation and manipulation tasks, trained on a single video. However, these single video GANs require unreasonable amount of time to train on a single video, rendering them almost impractical. In this paper we question the necessity of a GAN for generation from a single video, and introduce a non-parametric baseline for a variety of generation and manipulation tasks. We revive classical space-time patches-nearest-neighbors approaches and adapt them to a scalable unconditional generative model, without any learning. This simple baseline surprisingly outperforms single-video GANs in visual quality and realism (confirmed by quantitative and qualitative evaluations), and is disproportionately faster (runtime reduced from several days to seconds). Other than diverse video generation, we demonstrate other applications using the same framework, including video analogies and spatio-temporal retargeting. Our proposed approach is easily scaled to Full-HD videos. These observations show that the classical approaches, if adapted correctly, significantly outperform heavy deep learning machinery for these tasks. This sets a new baseline for single-video generation and manipulation tasks, and no less important – makes diverse generation from a single video practically possible for the first time.

N. Haim and B. Feinstein—Equal contribution.

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Notes

  1. 1.

    All quantitative comparisons were done on generated samples of the same resolution and video length as that of the other method.

  2. 2.

    Each cluster has an integer cluster index. We divide each index by the total number of clusters/bins to be in [0, 1].

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Acknowledgements

This project received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 788535), from the D. Dan and Betty Kahn Foundation, and from the Israel Science Foundation (grant 2303/20). Dr. Bagon is a Robin Chemers Neustein AI Fellow.

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Haim, N. et al. (2022). Diverse Generation from a Single Video Made Possible. 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 13677. Springer, Cham. https://doi.org/10.1007/978-3-031-19790-1_30

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