Symmetry-Aware Semantic Segmentation of a 3D Human Body Model

Abstract

<p indent="0mm">An automatic segmentation method for a 3D human model is proposed for the symmetry-aware semantic segmentation. Firstly, a symmetry-aware kinematic skeleton is extracted from the input human body model via template skeleton embedding. Secondly, the number of the segments is determined by the kinematic skeleton; some key points corresponding to the segments are extracted meanwhile. Thirdly, while taking the key points as the boundary constraints, the harmonic fields are constructed to obtain a set of isolines as the potential cutting boundaries. Finally, the key points are taken as the clustering centers to conduct a spectral clustering operation, which guides the selections of the cutting boundaries. In this way, the semantic information in the kinematic skeleton can be directly transferred to the segments, and the number of segments can be controlled by the structure of the kinematic skeleton. Results on the SCAPE dataset, the MPI-FAUST dataset and the Princeton Segmentation Benchmark show that the proposed method can automatically achieve symmetry-aware semantic segmentation, and is robust to the shape and the pose of the human body model.