  One-shot talking face generation aims at synthesizing a high-quality talking
face video from an arbitrary portrait image, driven by a video or an audio
segment. One challenging quality factor is the resolution of the output video:
higher resolution conveys more details. In this work, we investigate the latent
feature space of a pre-trained StyleGAN and discover some excellent spatial
transformation properties. Upon the observation, we explore the possibility of
using a pre-trained StyleGAN to break through the resolution limit of training
datasets. We propose a novel unified framework based on a pre-trained StyleGAN
that enables a set of powerful functionalities, i.e., high-resolution video
generation, disentangled control by driving video or audio, and flexible face
editing. Our framework elevates the resolution of the synthesized talking face
to 1024*1024 for the first time, even though the training dataset has a lower
resolution. We design a video-based motion generation module and an audio-based
one, which can be plugged into the framework either individually or jointly to
drive the video generation. The predicted motion is used to transform the
latent features of StyleGAN for visual animation. To compensate for the
transformation distortion, we propose a calibration network as well as a domain
loss to refine the features. Moreover, our framework allows two types of facial
editing, i.e., global editing via GAN inversion and intuitive editing based on
3D morphable models. Comprehensive experiments show superior video quality,
flexible controllability, and editability over state-of-the-art methods.
