Niv Granot — accepted AI-conference papers

2022

Diverse Generation from a Single Video Made Possible

Niv Haim, Ben Feinstein, Niv Granot, Assaf Shocher, Shai Bagon, Tali Dekel +1

ECCV 2022poster

"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 vi…

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2022

Drop the GAN: In Defense of Patches Nearest Neighbors As Single Image Generative Models

Niv Granot, Ben Feinstein, Assaf Shocher, Shai Bagon, Michal Irani

CVPR 2022oral

Image manipulation dates back long before the deep learning era. The classical prevailing approaches were based on maximizing patch similarity between the input and generated output. Recently, single-image GANs were introduced as a superior and more sophisticated solution to image manipulation tasks…

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