Coherent Online Video Style Transfer

Dongdong Chen, Jing Liao, Lu Yuan, Nenghai Yu, Gang Hua; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1105-1114


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 real-time. 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.

Related Material

[pdf] [arXiv]
author = {Chen, Dongdong and Liao, Jing and Yuan, Lu and Yu, Nenghai and Hua, Gang},
title = {Coherent Online Video Style Transfer},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}