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FaceLift: Semi-supervised 3D Facial Landmark Localization
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?
Few-shot Geometry-Aware Keypoint Localization
Supervised keypoint localization methods rely on large manually labeled image datasets, where objects can deform, articulate, or occlude.
Multi-Domain Multi-Definition Landmark Localization for Small Datasets
Training a small dataset alongside a large(r) dataset helps with robust learning for the former, and provides a universal mechanism for facial landmark localization for new and/or smaller standard datasets.
Generative Landmarks
We propose a general purpose approach to detect landmarks with improved temporal consistency, and personalization.
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