Dense Semantic and Topological Correspondence of 3D Faces without Landmarks

Many previous literatures use landmarks to guide the correspondence of 3D faces. However, these landmarks, either manually or automatically annotated, are hard to define consistently across different faces in many circumstances. We propose a general framework for dense correspondence of 3D faces without landmarks in this paper. The dense correspondence goal is revisited in two perspectives: semantic and topological correspondence. Starting from a template facial mesh, we sequentially perform global alignment, primary correspondence by template warping, and contextual mesh refinement, to reach the final correspondence result. The semantic correspondence is achieved by a local iterative closest point (ICP) algorithm of kernelized version, allowing accurate matching of local features. Then, robust deformation from the template to the target face is formulated as a minimization problem. Furthermore, this problem leads to a well-posed sparse linear system such that the solution is unique and efficient. Finally, a contextual mesh refining algorithm is applied to ensure topological correspondence. In the experiment, the proposed method is evaluated both qualitatively and quantitatively on two datasets including a publicly available FRGC v2.0 dataset, demonstrating reasonable and reliable correspondence results.