StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pretrained StyleGAN

• URL: http://arxiv.org/abs/2203.04036v1
• Date: Tue, 8 Mar 2022 12:06:12 GMT
• Title: StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pretrained StyleGAN
• Authors: Fei Yin and Yong Zhang and Xiaodong Cun and Mingdeng Cao and Yanbo Fan and Xuan Wang and Qingyan Bai and Baoyuan Wu and Jue Wang and Yujiu Yang
• Abstract summary: One-shot talking face generation aims at synthesizing a high-quality talking face video from an arbitrary portrait image. In this work, we investigate the latent feature space of a pre-trained StyleGAN and discover some excellent spatial transformation properties. We propose a novel unified framework based on a pre-trained StyleGAN that enables a set of powerful functionalities.
• Score: 49.917296433657484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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