Dense Face Alignment

Dense face alignment aims to accurately locate a large number of 3D facial landmarks from a single 2D image, enabling precise 3D face reconstruction and analysis. Recent research focuses on improving robustness to occlusions and varying viewpoints by employing hybrid models that fuse information from convolutional neural networks (CNNs) processing image features with graph convolutional networks (GCNs) leveraging geometric relationships between landmarks, or by integrating 2D image-space predictions with 3D model-space information. These advancements are significant for applications in areas such as facial animation, virtual reality, and medical imaging, where accurate 3D facial representations are crucial.