At present, face 3D reconstruction has broad application prospects in various
fields, but the research on it is still in the development stage. In this
paper, we hope to achieve better face 3D reconstruction quality by combining
multi-view training framework with face parametric model Flame, propose a
multi-view training and testing model MFNet (Multi-view Flame Network). We
build a self-supervised training framework and implement constraints such as
multi-view optical flow loss function and face landmark loss, and finally
obtain a complete MFNet. We propose innovative implementations of multi-view
optical flow loss and the covisible mask. We test our model on AFLW and
facescape datasets and also take pictures of our faces to reconstruct 3D faces
while simulating actual scenarios as much as possible, which achieves good
results. Our work mainly addresses the problem of combining parametric models
of faces with multi-view face 3D reconstruction and explores the implementation
of a Flame based multi-view training and testing framework for contributing to
the field of face 3D reconstruction.

通过将多视图训练框架与面部参数模型 Flame 相结合，本论文旨在实现更好的面部 3D 重建质量，提出了一个多视图训练和测试模型 MFNet（多视图 Flame 网络）。通过建立自监督训练框架，引入多视图光流损失函数和面部标志损失等约束，最终获得了完整的 MFNet。基于 AFLW 和 facescape 数据集以及模拟实际场景拍摄的面部照片，我们在尽可能模拟实际情况的同时重建了 3D 面部，并取得了良好的结果。本研究主要解决了将面部参数模型与多视角面部 3D 重建相结合的问题，并探索了基于 Flame 的多视图训练和测试框架的实现，对于面部 3D 重建领域有一定的贡献。