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. Inthis 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 alignmentalgorithm is proposed to align human body models to break symmetric ambiguity. A symmetry-awaredescriptor is proposed based on the structure of the human body models, which is robust to both pose and shape variations in human body models. AnAdaBoost 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.

In multi-view stereo, unreliable matching in low-textured regions has a negative impact on the completeness of reconstructed models. Since the photometric consistency of low-textured regions is not discriminative under a local window, non-local information provided by the Markov Random Field (MRF) model can alleviate the matching ambiguity but is limited in continuous space with high computational complexity. Owing to its sampling and propagation strategy, PatchMatch multi-view stereo methods have advantages in terms of optimizing the continuous labeling problem. In this paper, we propose a novel method to address this problem, namely the Coarse-Hypotheses Guided Non-Local PatchMatch Multi-View Stereo (CNLPA-MVS), which takes the advantages of both MRF-based non-local methods and PatchMatch multi-view stereo and compensates for their defects mutually. First, we combine dynamic programing (DP) and sequential propagation along scanlines in parallel to perform CNLPA-MVS, thereby obtaining the optimal depth and normal hypotheses. Second, we introduce coarse inference within a universal window provided by winner-takes-all to eliminate the stripe artifacts caused by DP and improve completeness. Third, we add a local consistency strategy based on the hypotheses of similar color pixels sharing approximate values into CNLPA-MVS for further improving completeness. CNLPA-MVS was validated on public benchmarks and achieved state-of-the-art performance with high completeness.