TY - GEN
T1 - Evolvement Constrained Adversarial Learning for Video Style Transfer
AU - Li, Wenbo
AU - Wen, Longyin
AU - Bian, Xiao
AU - Lyu, Siwei
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Video style transfer is a useful component for applications such as augmented reality, non-photorealistic rendering, and interactive games. Many existing methods use optical flow to preserve the temporal smoothness of the synthesized video. However, the estimation of optical flow is sensitive to occlusions and rapid motions. Thus, in this work, we introduce a novel evolve-sync loss computed by evolvements to replace optical flow. Using this evolve-sync loss, we build an adversarial learning framework, termed as Video Style Transfer Generative Adversarial Network (VST-GAN), which improves upon the MGAN method for image style transfer for more efficient video style transfer. We perform extensive experimental evaluations of our method and show quantitative and qualitative improvements over the state-of-the-art methods.
AB - Video style transfer is a useful component for applications such as augmented reality, non-photorealistic rendering, and interactive games. Many existing methods use optical flow to preserve the temporal smoothness of the synthesized video. However, the estimation of optical flow is sensitive to occlusions and rapid motions. Thus, in this work, we introduce a novel evolve-sync loss computed by evolvements to replace optical flow. Using this evolve-sync loss, we build an adversarial learning framework, termed as Video Style Transfer Generative Adversarial Network (VST-GAN), which improves upon the MGAN method for image style transfer for more efficient video style transfer. We perform extensive experimental evaluations of our method and show quantitative and qualitative improvements over the state-of-the-art methods.
UR - https://www.scopus.com/pages/publications/85066781711
U2 - 10.1007/978-3-030-20887-5_15
DO - 10.1007/978-3-030-20887-5_15
M3 - Conference contribution
AN - SCOPUS:85066781711
SN - 9783030208868
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 232
EP - 248
BT - Computer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers
A2 - Schindler, Konrad
A2 - Mori, Greg
A2 - Li, Hongdong
A2 - Jawahar, C.V.
PB - Springer Verlag
T2 - 14th Asian Conference on Computer Vision, ACCV 2018
Y2 - 2 December 2018 through 6 December 2018
ER -