3D Human Face Reconstruction with 3DMM face model from RGB image
Not on the map. Why?
Abstract (arXiv)
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
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 5 May 2026.
Builds on
9 of 15 references.
References · 9
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
- 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 903
- 2017How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)cited by 525
- 2019Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Setcited by 482
- 2019Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoningcited by 420
- 2018Nonlinear 3D Face Morphable Modelcited by 157
- 2017S3FD: Single Shot Scale-invariant Face Detectorcited by 142
- 2016Learning Detailed Face Reconstruction from a Single Imagecited by 71
Built on by
Not cited yet.