Papers › Artistic style transfer for videos

Artistic style transfer for videos

28 Apr 2016arXiv:1604.08610archive 2025-07-28

Manuel Ruder, Alexey Dosovitskiy, Thomas Brox

In the past, manually re-drawing an image in a certain artistic style required a professional artist and a long time. Doing this for a video sequence single-handed was beyond imagination. Nowadays computers provide new possibilities. We present an approach that transfers the style from one image (for example, a painting) to a whole video sequence. We make use of recent advances in style transfer in still images and propose new initializations and loss functions applicable to videos. This allows us to generate consistent and stable stylized video sequences, even in cases with large motion and strong occlusion. We show that the proposed method clearly outperforms simpler baselines both qualitatively and quantitatively.

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manuelruder/artistic-videos officialmentioned in papermentioned on GitHubtorchNOASSERTION report
anitagold/60-days-of-Udacity mentioned on GitHubpytorch report
cysmith/neural-style-tf mentioned on GitHubtfGPL-3.0 report

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Style Transfer

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