3 accepted papers
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 sp…
Supervised keypoint localization methods rely on large manually labeled image datasets, where objects can deform, articulate, or occlude. However, creating such large keypoint labels is time-consuming and costly, and is often error-prone due to inconsistent labeling. Thus, we desire an approach that…
"We present a novel method for multi image domain and multi-landmark definition learning for small dataset facial localization. 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…