Nowadays as convolution neural networks demonstrate its powerful problem-solving ability in the area of image processing, efforts have been made to reconstruct detailed face shapes from 2D face images or videos. However, to make the full use of CNN, a large number of labeled data is required to train the network. Coarse morphable face model has been used to synthesize labeled data. However, it is hard for coarse morphable face models to generate photo-realistic data with detail such as wrinkles. In this project, we present a pipeline that reconstructs a human face 3D model from a single RGB image. The pipeline includes face detection, landmark detection, regression of 3DMM model parameters, and soft rendering. Mentor: Zhipeng Fan (Email: [email protected]) Code Repository: https://github.com/SeVEnMY/3d-face- reconstruction Code Reference: https://github.com/sicxu/Deep3DFaceRecon pytorch

翻译：如今，卷积神经网络在图像处理领域展现出强大的问题解决能力，研究人员已尝试从二维人脸图像或视频中重建精细的人脸形状。然而，要充分利用卷积神经网络，需要大量标注数据来训练网络。粗糙的可变形人脸模型已被用于合成标注数据，但该类模型难以生成具有皱纹等细节的高度逼真数据。本项目中，我们提出了一套从单张RGB图像重建人脸三维模型的完整流程，该流程包括人脸检测、关键点检测、三维形变模型参数回归以及软渲染。导师：樊志鹏（邮箱：[email protected]）代码仓库：https://github.com/SeVEnMY/3d-face-reconstruction 参考代码：https://github.com/sicxu/Deep3DFaceRecon pytorch