High-Fidelity Face Reenactment Via Identity-Matched Correspondence Learning

Face reenactment aims to generate an animation of a source face using the poses and expressions from a target face. Although recent methods have made remarkable progress by exploiting generative adversarial networks, they are limited in generating high-fidelity and identity-preserving results due to the inappropriate driving information and insufficiently effective animating strategies. In this work, we propose a novel face reenactment framework that achieves both high-fidelity generation and identity preservation. Instead of sparse face representations (e.g., facial landmarks and keypoints), we utilize the Projected Normalized Coordinate Code (PNCC) to better preserve facial details. We propose to reconstruct the PNCC with the source identity parameters and the target pose and expression parameters estimated by 3D face reconstruction to factor out the target identity. By adopting the reconstructed representation as the driving information, we address the problem of identity mismatch. To effectively utilize the driving information, we establish the correspondence between the reconstructed representation and the source representation based on the features extracted by an encoder network. This identity-matched correspondence is then utilized to animate the source face using a novel feature transformation strategy. The generator network is further enhanced by the proposed geometry-aware skip connection. Once trained, our model can be applied to previously unseen faces without further training or fine-tuning. Through extensive experiments, we demonstrate the effectiveness of our method in face reenactment and show that our model outperforms state-of-the-art approaches both qualitatively and quantitatively. Additionally, the proposed PNCC reconstruction module can be easily inserted into other methods and improve their performance in cross-identity face reenactment.