Automatic location and semantic labeling of landmarks on 3D human body models

Landmarks on human body models are of great significance for applications such as digital anthropometry and clothing design. The diversity of pose and shape of human body models and the semantic gap make landmarking a challenging problem. In this paper, a learning-based method is proposed to locate landmarks on human body models by analyzing the relationship between geometric descriptors and semantic labels of landmarks. A shape alignment algorithm is proposed to align human body models to break symmetric ambiguity. A symmetry-aware descriptor is proposed based on the structure of the human body models, which is robust to both pose and shape variations in human body models. An AdaBoost regression algorithm is adopted to establish the correspondence between several descriptors and semantic labels of the landmarks. Quantitative and qualitative analyses and comparisons show that the proposed method can obtain more accurate landmarks and distinguish symmetrical landmarks semantically. Additionally, a dataset of landmarked human body models is also provided, containing 271 human body models collected from current human body datasets; each model has 17 landmarks labeled manually.