3D Human Face Reconstruction with 3DMM face model from RGB image
For researchers in computer vision and graphics, this work offers an incremental improvement in 3D face reconstruction by combining existing 3DMM regression with soft rendering, but lacks novel contributions or quantitative results.
This project presents a pipeline for reconstructing a 3D human face model from a single RGB image using a 3D morphable model (3DMM) and deep learning, achieving detailed shape recovery without requiring photo-realistic training data. No concrete performance numbers are provided.
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: zf606@nyu.edu) Code Repository: https://github.com/SeVEnMY/3d-face- reconstruction Code Reference: https://github.com/sicxu/Deep3DFaceRecon pytorch