Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency[ECCV 2020]
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This is an official python implementation of MGCNet. This is the pre-print version https://arxiv.org/abs/2007.12494.
video
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Full video can be seen in [YouTube] https://www.youtube.com/watch?v=DXzkO3OwlYQ
We run the code with python3.7, tensorflow 1.13
bash git clone --recursive https://github.com/jiaxiangshang/MGCNet.git cd MGCNet (sudo) pip install -r requirement.txt(1) For render loss(reconstruction loss), we use the differential renderer named tfmeshrender(thanks!) https://github.com/google/tfmeshrenderer. I find many issue happens here, so let's make this more clear. The tfmeshrender does not return triangle id for each pixel after rasterise, we do this by our self and add these changes as submodule to mgcnet.
(2) Then how to compile tfmeshrender, my setting is bazel==10.1, gcc==5., the compile command is
bash bazel build ...The gcc/g++ version higher than 5. will bring problems, a good solution is virtual environment with a gcc maybe 5.5. If the The gcc/g++ version is 4.* that you can try to change the compile cmd in BUILD file, about the flag -DGLIBCXXUSECXX11ABI=0 or -DGLIBCXXUSECXX11ABI=1 for 4.* or 5.*
We include BFM09/BFM09 expression, BFM09 face region from https://github.com/microsoft/Deep3DFaceReconstruction, BFM09 uv from https://github.com/anilbas/3DMMasSTN into a whole 3dmm model. https://drive.google.com/file/d/1RkTgcSGNs2VglHriDnyr6ZS5pbnZrUnV/view?usp=sharing Extract this file to /MGCNet/model 2. pretain
This include the pretrail model for the Resnet50 and vgg pretrain model for Facenet. https://drive.google.com/file/d/1jVlf05BmnbIQXZRfmz-dA03xGCawBw/view?usp=sharing Extract this file to /MGCNet/pretain
Extract this file to /MGCNet/data, we can not provide all datas, as it is too large and the license of MPIE dataset http://www.cs.cmu.edu/afs/cs/project/PIE/MultiPie/Multi-Pie/Home.html not allow me to do this.
The detection method from https://github.com/1adrianb/2D-and-3D-face-alignment, and we use the SFD face detector
I get this part code from Yu DENG([email protected]), maybe you can ask help from him.
test_image.py This is used to inference a single unprocessed image(cmd in file). This file can also render the images(geometry, texture, shading,multi-pose), like above or in our paper(read code), which makes visualization and comparison more convenient.
preprocess All the preprocess has been included in 'testimage.py', we show the outline here. (1) face detection and face alignment are package in ./tools/preprocess/detectlandmark,py. (2) face alignment by affine transformation to warp the unprocess image. Test all the images in a folder can follow this preprocess.
If you use this code, please consider citing:
@article{shang2020self, title={Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency}, author={Shang, Jiaxiang and Shen, Tianwei and Li, Shiwei and Zhou, Lei and Zhen, Mingmin and Fang, Tian and Quan, Long}, journal={arXiv preprint arXiv:2007.12494}, year={2020} }
Please contact [email protected] or open an issue for any questions or suggestions.