Generalizable Face Landmarking Guided by Conditional Face Warping
Authors: Jiayi Liang, Haotian Liu, Hongteng Xu, Dixin Luo

As a significant step for human face modeling editing and generation face landmarking aims at extracting facial keypoints from images. A generalizable face landmarker is required in practice because real-world facial images e.g. the avatars in animations and games are often stylized in various ways. However achieving generalizable face landmarking is challenging due to the diversity of facial styles and the scarcity of labeled stylized faces. In this study we propose a simple but effective paradigm to learn a generalizable face landmarker based on labeled real human faces and unlabeled stylized faces. Our method learns the face landmarker as the key module of a conditional face warper. Given a pair of real and stylized facial images the conditional face warper predicts a warping field from the real face to the stylized one in which the face landmarker predicts the ending points of the warping field and provides us with high-quality pseudo landmarks for the corresponding stylized facial images. Applying an alternating optimization strategy we learn the face landmarker to minimize i) the discrepancy between the stylized faces and the warped real ones and ii) the prediction errors of both real and pseudo landmarks. Experiments on various datasets show that our method outperforms existing state-of-the-art domain adaptation methods in face landmarking tasks leading to a face landmarker with better generalizability. Code is available at https://plustwo0.github.io/project-face-landmarker.