Self-supervised 3D face reconstruction based on multi-view UV fusion

Image-based 3D face reconstruction has a huge application in the field of face analysis, such as face recognition, facial animation, and face editing. Recently, the popular methods based on 3dmm suffer from the ill-posed face pose16 and depth ambiguity issue. In order to address the issue, two multi-view geometric constraints are included in the reconstruction process. Note that before using these two constraints, a complete UV texture needs to be generated by texture fusion. Then, we can establish dense correspondences between different views leveraging a novel self-supervised pixel consistency constraint. We also use the facial landmark-based epi-polar constraint to constrain the pose between different views to obtain more accurate results. Extensive experiments demonstrate the superiority of the proposed method over other popular 3dmm methods with single-view input in accuracy and robustness, especially under large poses.