46,568 research outputs found
Automatic landmark annotation and dense correspondence registration for 3D human facial images
Dense surface registration of three-dimensional (3D) human facial images
holds great potential for studies of human trait diversity, disease genetics,
and forensics. Non-rigid registration is particularly useful for establishing
dense anatomical correspondences between faces. Here we describe a novel
non-rigid registration method for fully automatic 3D facial image mapping. This
method comprises two steps: first, seventeen facial landmarks are automatically
annotated, mainly via PCA-based feature recognition following 3D-to-2D data
transformation. Second, an efficient thin-plate spline (TPS) protocol is used
to establish the dense anatomical correspondence between facial images, under
the guidance of the predefined landmarks. We demonstrate that this method is
robust and highly accurate, even for different ethnicities. The average face is
calculated for individuals of Han Chinese and Uyghur origins. While fully
automatic and computationally efficient, this method enables high-throughput
analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl
Multi-Domain Pose Network for Multi-Person Pose Estimation and Tracking
Multi-person human pose estimation and tracking in the wild is important and
challenging. For training a powerful model, large-scale training data are
crucial. While there are several datasets for human pose estimation, the best
practice for training on multi-dataset has not been investigated. In this
paper, we present a simple network called Multi-Domain Pose Network (MDPN) to
address this problem. By treating the task as multi-domain learning, our
methods can learn a better representation for pose prediction. Together with
prediction heads fine-tuning and multi-branch combination, it shows significant
improvement over baselines and achieves the best performance on PoseTrack ECCV
2018 Challenge without additional datasets other than MPII and COCO.Comment: Extended abstract for the ECCV 2018 PoseTrack Worksho
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