Dense Semantic and Topological Correspondence of 3D Faces without Landmarks

Zhenfeng Fan, Xiyuan Hu, Chen Chen, Silong Peng; The European Conference on Computer Vision (ECCV), 2018, pp. 523-539

Many previous literatures use landmarks to guide the cor- respondence of 3D faces. However, these landmarks, either manually or automatically annotated, are hard to define consistently across differ- ent 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 tem- plate warping, and contextual mesh refinement, to reach the final cor- respondence 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. Fur- thermore, 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.