Abstract. 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 re-
fining 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