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Portrait synthesis creates realistic digital avatars which enable users tointeract with others in a compelling way. Recent advances in StyleGAN and itsextensions have shown promising results in synthesizing photorealistic andaccurate reconstruction of human faces. However, previous methods often focuson frontal face synthesis and most methods are not able to handle large headrotations due to the training data distribution of StyleGAN. In this work, ourgoal is to take as input a monocular video of a face, and create an editabledynamic portrait able to handle extreme head poses. The user can create novelviewpoints, edit the appearance, and animate the face. Our method utilizespivotal tuning inversion (PTI) to learn a personalized video prior from amonocular video sequence. Then we can input pose and expression coefficients toMLPs and manipulate the latent vectors to synthesize different viewpoints andexpressions of the subject. We also propose novel loss functions to furtherdisentangle pose and expression in the latent space. Our algorithm shows muchbetter performance over previous approaches on monocular video datasets, and itis also capable of running in real-time at 54 FPS on an RTX 3080.

PVP: Personalized Video Prior for Editable Dynamic Portraits using StyleGAN