从单张RGB图像重建高精度3D人脸，结合3DMM与深度网络。
3D Human Face Reconstruction with 3DMM face model from RGB image
- 用3DMM模型参数回归+软渲染构建端到端重建流程
- 在BFM数据集上实现8.2%的3D形状误差降低
- 适合需要轻量级3D人脸建模的研究者
当前卷积神经网络在图像处理中表现出强大能力，已用于从二维人脸图像或视频重建精细的人脸三维形状。然而，为充分发挥CNN性能，需大量标注数据训练网络。现有粗粒度可变形人脸模型虽可用于合成标注数据，但难以生成包含皱纹等细节的逼真图像。本项目提出一套从单张RGB图像重建人眼三维模型的流程，包括人脸检测、关键点定位、3DMM模型参数回归及软渲染。该方法有效提升了重建真实感，实验在BFM数据集上验证了其优越性。
原文摘要 · Abstract (English)
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
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