由条件人脸变形引导的泛化性人脸标志点检测
Generalizable Face Landmarking Guided by Conditional Face Warping
AI总结：
提出基于条件人脸变形的范式，利用真实和风格化人脸学习泛化性人脸标志点检测器，通过交替优化最小化差异和预测误差，在多个数据集上优于现有域自适应方法。
摘要：
作为人脸建模、编辑和生成的重要步骤，人脸标志点检测旨在从图像中提取面部关键点。实践中需要泛化性的人脸标志点检测器，因为现实世界的面部图像（例如动画和游戏中的虚拟形象）通常以各种方式被风格化。然而，由于面部风格的多样性和带标签的风格化人脸稀缺，实现泛化性的人脸标志点检测具有挑战性。在本研究中，我们提出了一种简单但有效的范式，基于带标签的真实人脸和未带标签的风格化人脸来学习泛化性的人脸标志点检测器。我们的方法将人脸标志点检测器作为条件人脸变形器的关键模块进行学习。给定一对真实和风格化的面部图像，条件人脸变形器预测从真实人脸到风格化人脸的变形场，其中人脸标志点检测器预测变形场的终点，并为我们提供对应风格化面部图像的高质量伪标志点。应用交替优化策略，我们学习人脸标志点检测器以最小化 $i)$ 风格化人脸与变形后真实人脸之间的差异，以及 $ii)$ 真实和伪标志点的预测误差。在多个数据集上的实验表明，我们的方法在人脸标志点检测任务中优于现有的最先进域自适应方法，从而得到具有更好泛化性的人脸标志点检测器。代码可在 https://plustwo0.github.io/project-face-landmarker 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
