Face alignment and reconstruction are classical problems in the computer vision field, one of the greatest difficulties of which is the limited number of facial images with landmark points. The 300 W-LP dataset is the most commonly used for the existing methods of single-view 3D Morphable Model (3DMM)-based reconstruction; however, the model performance is limited by the small variety of facial images in this dataset. In this work, a 3D facial image dataset with landmark points generated by the rotate-and-render method is proposed. The key innovation of the proposed method is that the back-and-forth rotation of faces in 3D space and then re-rendering them to the 2D plane can provide strong self-supervision. The recent advances in 3D face modeling and high-resolution generative adversarial networks (GANs) are leveraged to constitute the blocks. To obtain more precise facial landmark points, the 3D dense face alignment (3DDFA) model is used to label the generated images and filter the landmark points. Finally, the 3DDFA model is retrained using the proposed dataset, and an improved result is achieved.

人脸对齐和重建是计算机视觉领域的经典问题，其中最大的困难之一是具有标志点的人脸图像数量有限。300 W-LP 数据集是现有的基于单视图 3D Morphable Model (3DMM) 的重建方法最常用的数据集；然而，模型性能受到该数据集中面部图像种类较少的限制。在这项工作中，提出了一个具有由旋转和渲染方法生成的标志点的 3D 面部图像数据集。该方法的关键创新在于在 3D 空间中来回旋转人脸，然后将它们重新渲染到 2D 平面可以提供强大的自我监督。利用 3D 人脸建模和高分辨率生成对抗网络 (GAN) 的最新进展来构成块。为了获得更精确的面部标志点，使用3D密集面部对齐（3DDFA）模型对生成的图像进行标记并过滤标志点。最后，使用所提出的数据集重新训练 3DDFA 模型，并获得了改进的结果。