论文标题
通过联合网格和地标回归对人的面孔的透视重建
Perspective Reconstruction of Human Faces by Joint Mesh and Landmark Regression
论文作者
论文摘要
即使3D面重建取得了令人印象深刻的进步，但由于在透视图下，由于面部非常接近摄像机，因此大多数基于正交投影的面部重建方法无法实现准确，一致的重建结果。在本文中，我们建议在世界空间中同时重建3D面部网格，并预测图像平面上的2D面部标记以解决透视图3D面部重建问题。根据预测的3D顶点和2D地标，PNP求解器可以很容易地估算6DOF（6个自由度）面姿势，以表示透视投影。我们的方法在ECCV 2022 WCPA挑战的领导板上获得了第一名，而我们的模型在不同的身份，表达式和姿势下在视觉上是可靠的。释放培训代码和模型以促进未来的研究。
Even though 3D face reconstruction has achieved impressive progress, most orthogonal projection-based face reconstruction methods can not achieve accurate and consistent reconstruction results when the face is very close to the camera due to the distortion under the perspective projection. In this paper, we propose to simultaneously reconstruct 3D face mesh in the world space and predict 2D face landmarks on the image plane to address the problem of perspective 3D face reconstruction. Based on the predicted 3D vertices and 2D landmarks, the 6DoF (6 Degrees of Freedom) face pose can be easily estimated by the PnP solver to represent perspective projection. Our approach achieves 1st place on the leader-board of the ECCV 2022 WCPA challenge and our model is visually robust under different identities, expressions and poses. The training code and models are released to facilitate future research.
