Automatic motion-guided video stylization and personalization

Authors:
Chen Cao;Shifeng Chen;Wei Zhang;Xiaoou Tang
Affiliations:
Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences, China, Shenzhen, China;Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences, China, Shenzhen, China;Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences, China, Shenzhen, China;The Chinese University of Hong Kong, Hong Kong, China
Venue:
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Year:
2011

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Abstract

Video stylization transfers a source video into an artistic version while maintaining temporal coherence between adjacent frames. In this paper, we formulate the unsupervised example-based video stylization with Markov random field model. In our algorithm, we implement an improved optical flow algorithm to maintain temporal coherence while improve the accuracy of estimation along motion boundaries. We also extend our algorithm to the application of video personalization, in which human faces keep clear and distinguishable. A series of techniques are fused in video personalization, including face detection and alignment, motion flow, skin detection, and illumination blending. Given a source video and a style template image, our algorithm produces the stylized and/or personalized video(s) automatically. Experimental results demonstrate that our algorithm performs excellently in both video stylization and personalization.