Learning facial structural dependency in 3D aligned space for face alignment

Facial structure's statistical characteristics offer pivotal prior information in facial landmark prediction, forming inter-dependencies among different landmarks. Such inter-dependencies ensure that predictions adhere to the shape distribution typical of natural faces. In challenging scenarios like occlusions or extreme facial poses, this structure becomes indispensable, which can help to predict elusive landmarks based on more discernible ones. While current deep learning methods do capture these landmark dependencies, it's often an implicit process heavily reliant on vast training datasets. We contest that such implicit modeling approaches fail to manage more challenging situations. In this paper, we propose a new method that harnesses the facial structure and explicitly explores inter-dependencies among facial landmarks in an end-to-end fashion. We propose a Structural Dependency Learning Module (SDLM). It uses 3D face information to map facial features into a canonical UV space, in which the facial structure is explicitly 3D semantically aligned. Besides, to explore the global relationships between facial landmarks, we take advantage of the self-attention mechanism in the image and UV spaces. We name the proposed method Facial Structure-based Face Alignment (FSFA). FSFA reinforces the landmark structure, especially under challenging conditions. Extensive experiments demonstrate that FSFA achieves state-of-the-art performance on the WFLW, 300W, AFLW, and COFW68 datasets.