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Coherent Online Video Style Transfer | IEEE Conference Publication | IEEE Xplore

Coherent Online Video Style Transfer


Abstract:

Training a feed-forward network for the fast neural style transfer of images has proven successful, but the naive extension of processing videos frame by frame is prone t...Show More

Abstract:

Training a feed-forward network for the fast neural style transfer of images has proven successful, but the naive extension of processing videos frame by frame is prone to producing flickering results. We propose the first end-to-end network for online video style transfer, which generates temporally coherent stylized video sequences in near realtime. Two key ideas include an efficient network by incorporating short-term coherence, and propagating short-term coherence to long-term, which ensures consistency over a longer period of time. Our network can incorporate different image stylization networks and clearly outperforms the per-frame baseline both qualitatively and quantitatively. Moreover, it can achieve visually comparable coherence to optimization-based video style transfer, but is three orders of magnitude faster.
Date of Conference: 22-29 October 2017
Date Added to IEEE Xplore: 25 December 2017
ISBN Information:
Electronic ISSN: 2380-7504
Conference Location: Venice, Italy

References

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