VIVE3D: Viewpoint-Independent Video Editing using 3D-Aware GANs
Anna Frühstück, Nikolaos Sarafianos, Yuanlu Xu, Peter Wonka, Tony Tung
Abstract
Figure 1 . We propose VIVE3D, a novel method that creates a powerful personalized 3D-aware generator using a low number of selected images of a target person. Given a new video of that person, we can faithfully modify several facial attributes as well as the camera viewpoint of the head crop. Finally, we seamlessly composite the edited face with the source frame in a temporally and spatially consistent manner, while retaining a plausible composition with the static components of the frame outside of the generator's region. The dotted squares in the center frame denote the reference regions for the three different camera poses in the column below.
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