Arbitrary Video Style Transfer via Multi-Channel Correlation | |
Deng, Yingying1,2,3; Tang, Fan4; Dong, Weiming1,2,3; Huang, Haibin5; Ma, Chongyang5; Xu, Changsheng1,2,3 | |
2021 | |
会议日期 | 2021-2 |
会议地点 | Vitual conference |
英文摘要 | Video style transfer is attracting increasing attention from the artificial intelligence community because of its numerous applications, such as augmented reality and animation production. Relative to traditional image style transfer, video style transfer presents new challenges, including how to effectively generate satisfactory stylized results for any specified style while maintaining temporal coherence across frames. Towards this end, we propose a Multi-Channel Correlation network (MCCNet), which can be trained to fuse exemplar style features and input content features for efficient style transfer while naturally maintaining the coherence of input videos to output videos. Specifically, MCCNet works directly on the feature space of style and content domain where it learns to rearrange and fuse style features on the basis of their similarity to content features. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48625] ![]() |
专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
通讯作者 | Tang, Fan; Dong, Weiming |
作者单位 | 1.CASIA-LLvision Joint Lab 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.NLPR, Institute of Automation, Chinese Academy of Sciences 4.School of Artificial Intelligence, Jilin University 5.Kuaishou Technology |
推荐引用方式 GB/T 7714 | Deng, Yingying,Tang, Fan,Dong, Weiming,et al. Arbitrary Video Style Transfer via Multi-Channel Correlation[C]. 见:. Vitual conference. 2021-2. |
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