  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.
