\(\text {Face2Face}^\rho \): Real-Time High-Resolution One-Shot Face Reenactment

Existing one-shot face reenactment methods either present obvious artifacts in large pose transformations, or cannot well-preserve the identity information in the source images, or fail to meet the requirements of real-time applications due to the intensive amount of computation involved. In this paper, we introduce \(\text {Face2Face}^\rho \), the first Real-time High-resolution and One-shot (RHO, \(\rho \)) face reenactment framework. To achieve this goal, we designed a new 3DMM-assisted warping-based face reenactment architecture which consists of two fast and efficient sub-networks, i.e., a u-shaped rendering network to reenact faces driven by head poses and facial motion fields, and a hierarchical coarse-to-fine motion network to predict facial motion fields guided by different scales of landmark images. Compared with existing state-of-the-art works, \(\text {Face2Face}^\rho \) can produce results of equal or better visual quality, yet with significantly less time and memory overhead. We also demonstrate that \(\text {Face2Face}^\rho \) can achieve real-time performance for face images of \(1440\times 1440\) resolution with a desktop GPU and \(256\times 256\) resolution with a mobile CPU.