Traditional approaches to retarget existing facial blendshape animations to other characters rely heavily on manually paired data including corresponding anchors, expressions, or semantic parametrizations to preserve the characteristics of the original performance. In this paper, inspired by recent developments in face swapping and reenactment, we propose a novel unsupervised learning method that reformulates the retargeting of 3D facial blendshape‐based animations in the image domain. The expressions of a source model is transferred to a target model via the rendered images of the source animation. For this purpose, a reenactment network is trained with the rendered images of various expressions created by the source and target models in a shared latent space. The use of shared latent space enable an automatic cross‐mapping obviating the need for manual pairing. Next, a blendshape prediction network is used to extract the blendshape weights from the translated image to complete the retargeting of the animation onto a 3D target model. Our method allows for fully unsupervised retargeting of facial expressions between models of different configurations, and once trained, is suitable for automatic real‐time applications.

High‐quality portrait image editing has been made easier by recent advances in GANs (e.g., StyleGAN) and GAN inversion methods that project images onto a pre‐trained GAN's latent space. However, extending the existing image editing methods, it is hard to edit videos to produce temporally coherent and natural‐looking videos. We find challenges in reproducing diverse video frames and preserving the natural motion after editing. In this work, we propose solutions for these challenges. First, we propose a video adaptation method that enables the generator to reconstruct the original input identity, unusual poses, and expressions in the video. Second, we propose an expression dynamics optimization that tweaks the latent codes to maintain the meaningful motion in the original video. Based on these methods, we build a StyleGAN‐based high‐quality portrait video editing system that can edit videos in the wild in a temporally coherent way at up to 4K resolution.