  We propose a novel facial Anchors and Contours Estimation framework, ACE-Net,
for fine-level face alignment tasks. ACE-Net predicts facial anchors and
contours that are richer than traditional facial landmarks while overcoming
ambiguities and inconsistencies in their definitions. We introduce a weakly
supervised loss enabling ACE-Net to learn from existing facial landmarks
datasets without the need for reannotation. Instead, synthetic data, from which
GT contours can be easily obtained, is used during training to bridge the
density gap between landmarks and true facial contours. We evaluate the face
alignment accuracy of ACE-Net with respect to the HELEN dataset which has 194
annotated facial landmarks, while it is trained with only 68 or 36 landmarks
from the 300-W dataset. We show that ACE-Net generated contours are better than
contours interpolated straight from the 68 GT landmarks and ACE-Net also
outperforms models trained only with full supervision from GT landmarks-based
contours.
