Artistic style transfer for videos
April 28, 2016 Β· Declared Dead Β· π German Conference on Pattern Recognition
"No code URL or promise found in abstract"
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Authors
Manuel Ruder, Alexey Dosovitskiy, Thomas Brox
arXiv ID
1604.08610
Category
cs.CV: Computer Vision
Citations
212
Venue
German Conference on Pattern Recognition
Last Checked
4 months ago
Abstract
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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