  3D facial landmark localization has proven to be of particular use for
applications, such as face tracking, 3D face modeling, and image-based 3D face
reconstruction. In the supervised learning case, such methods usually rely on
3D landmark datasets derived from 3DMM-based registration that often lack
spatial definition alignment, as compared with that chosen by hand-labeled
human consensus, e.g., how are eyebrow landmarks defined? This creates a gap
between landmark datasets generated via high-quality 2D human labels and 3DMMs,
and it ultimately limits their effectiveness. To address this issue, we
introduce a novel semi-supervised learning approach that learns 3D landmarks by
directly lifting (visible) hand-labeled 2D landmarks and ensures better
definition alignment, without the need for 3D landmark datasets. To lift 2D
landmarks to 3D, we leverage 3D-aware GANs for better multi-view consistency
learning and in-the-wild multi-frame videos for robust cross-generalization.
Empirical experiments demonstrate that our method not only achieves better
definition alignment between 2D-3D landmarks but also outperforms other
supervised learning 3D landmark localization methods on both 3DMM labeled and
photogrammetric ground truth evaluation datasets. Project Page:
https://davidcferman.github.io/FaceLift
