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We introduce VIVE3D, a novel approach that extends the capabilities ofimage-based 3D GANs to video editing and is able to represent the input videoin an identity-preserving and temporally consistent way. We propose two newbuilding blocks. First, we introduce a novel GAN inversion techniquespecifically tailored to 3D GANs by jointly embedding multiple frames andoptimizing for the camera parameters. Second, besides traditional semantic faceedits (e.g. for age and expression), we are the first to demonstrate edits thatshow novel views of the head enabled by the inherent properties of 3D GANs andour optical flow-guided compositing technique to combine the head with thebackground video. Our experiments demonstrate that VIVE3D generateshigh-fidelity face edits at consistent quality from a range of cameraviewpoints which are composited with the original video in a temporally andspatially consistent manner.

VIVE3D: Viewpoint-Independent Video Editing using 3D-Aware GANs