Symmetry-Aware Human Shape Correspondence Using Skeleton

In this paper, we propose a symmetry-aware human shape correspondence extraction method. We address the symmetric flip problem which exists in establishing correspondences for intrinsically symmetric models and improve the accuracy of the final corresponding pairs. To achieve this goal, we extended the state-of-the-art approach by using skeleton information to further remove symmetric flipped shape correspondences. Traditional approaches that only rely on surface geometry information can hardly discriminate surface points which are symmetric. With the appearance of inexpensive RGB-D camera, such as Kinect, skeleton information can be easily obtained along with mesh. Therefore, after the initial correspondences are achieved, we extend the candidate sets for each point on the template, followed by making use of skeleton to remove the symmetric flipped false candidates. In the remaining candidates, final correspondences are achieved by choosing those with minimum geodesic distortion from base vertex set, which is formed by sampling on the mesh. Experiments demonstrate that the proposed method can effectively remove all the symmetric flipped candidates. Moreover, the final correspondence pair is more accurate than those of the state of the arts.