High-Fidelity Face Reenactment Via Identity-Matched Correspondence Learning

作者：Han Xue, Jun Ling, Anni Tang, Li Song, Rong Xie, Wenjun Zhang · 发表于：ACM Trans. Multim. Comput. Commun. Appl. · 年份：2022 · DOI：10.1145/3571857 · 被引用次数：15 · 研究领域：Computer Science

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 fu...