Learning human shape model from multiple databases with correspondence considering kinematic consensus

In various applications of computer graphics and model-based computer vision, a human shape model cannot only model the kinematic properties of a subject to drive the mesh into various postures, but it can also be utilized to parameterize the shape variations across individuals. It is of great benefit to improve the diversity of the training databases by learning the model from multiple databases, once the correspondences among scans of these databases can be achieved. To accomplish this goal, we proposed a framework to match the scans from multiple databases, using the assistance of kinematic properties, to compute the correspondences. The resulting correspondence is accurate, robust, capable of handling scan incompletion, and is homogeneous across shapes and postures. In our approach, we start with evaluating how a correspondence, which is achieved via minimizing the deformation energy, agrees with the kinematic properties, and then, we jointly fit the source scans to the target scans to derive the correspondences between the databases. The extensive results show that our approach can generate a faithful correspondence even in extreme cases, without carefully selecting the deformation factors and markers. We also developed a method, with which a commendable and predictable result can be synthesized, to control the rendered shape in an intuitive way.