A Unified Multi-output Semi-supervised Network for 3D Face Reconstruction
Pengrui Wang, Yi Tian, Wujun Che, Bo Xu · 2019
In this paper, we propose a method to reconstruct fine-grained 3D faces from single images base on a nearly unified multi-output regression network. The network estimates the facial shape, normal and appearance jointly in 2D UV map which preserves spatial adjacency relations among vertexes and provides semantic meaning of each vertex. Three contributions of the proposed method are: 1) we generate the UV map by as-rigid-as-possible parametrization to address the overlapping problem caused by cylindrical unwarp; 2) we directly estimate face normal rather than compute it from the estimated shape to let it catch geometric details from face texture; 3) we propose a post process strategy to generating more realistic faces and to employing the estimated normal. Experiments show that our network is able to learn a uniform appearance and predict more accurate shape from the proposed UV map. Additionally, the post process procedure can improve the quality of facial shapes and add geometric details from estimated normals.