Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set

Pytorch version of the repo Deep3DFaceReconstruction.

This repo only contains the reconstruction part, so you can use Deep3DFaceReconstruction-pytorch repo to train the network. And the pretrained model is also from this repo.

Features

MTCNN

I use mtcnn to crop raw images and detect 5 landmarks. The most code of MTCNN comes from FaceNet-pytorch.

Pytorc3d

In this repo, I use PyTorch3d 0.3.0 to render the reconstructed images.

Estimating Intrinsic Parameters

In the origin repo (Deep3DFaceReconstruction-pytorch), the rendered images is not the same as the input image because of preprocess. So, I add the estimate_intrinsic to get intrinsic parameters.

Examples:

Here are some examples:

Origin Images	Cropped Images	Rendered Images

File Architecture

├─BFM               same as Deep3DFaceReconstruction
├─dataset           storing the corpped images
│  └─Vladimir_Putin
├─examples          show examples
├─facebank          storing the raw/origin images
│  └─Vladimir_Putin
├─models            storing the pretrained models
├─output            storing the output images(.mat, .png)
│  └─Vladimir_Putin
└─preprocess        cropping images and detecting landmarks
    ├─data          storing the models of mtcnn
    ├─utils

Also, this repo can also generate the UV map, and you need download UV coordinates from the following link:
  Download UV coordinates fom STN website: https://github.com/anilbas/3DMMasSTN/blob/master/util/BFM_UV.mat
  Copy BFM_UV.mat to BFM

The pretrained models can be downloaded from Google Drive.