UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing

Recent research has witnessed advances in facial image editing tasks including face swapping and face reenactment. However, these methods are confined to dealing with one specific task at a time. In addition, for video facial editing, previous methods either simply apply transformations frame by fra...

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Veröffentlicht in:	IEEE transactions on image processing 2021, Vol.30, p.6107-6116
Hauptverfasser:	Cao, Meng, Huang, Haozhi, Wang, Hao, Wang, Xuan, Shen, Li, Wang, Sheng, Bao, Linchao, Li, Zhifeng, Luo, Jiebo
Format:	Artikel
Sprache:	eng
Schlagworte:	3D temporal loss dynamic training sample selection Editing Faces Facial video editing Image reconstruction Interpolation Iterative methods Optical losses region-aware conditional normalization Solid modeling Task analysis Three dimensional models Three-dimensional displays Training
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