/3DDFA_V2

the 3DDFA_V2 with 81 face landmarks.

Primary LanguagePythonMIT LicenseMIT

Towards Fast, Accurate and Stable 3D Dense Face Alignment

在原有项目的基础上,将68个人脸关键点拓展到81个,另外13个为额头关键点。

感谢cleardusk的工作

Requirements

Build dependencies by referring to the original project

git clone https://github.com/chenjun2hao/3DDFA_V2.git
cd 3DDFA_V2

Run demos

# 1. origin 68 landmarks
python demo.py -c configs/mb1_120x120.yml -f examples/inputs/emma.jpg -o 2d_sparse # -o [2d_sparse, 2d_dense, 3d, depth, pncc, pose, uv_tex, ply, obj]

# 2. the 81 landmarks
python demo.py -c configs/mb1_120x120_81landmarks.yml -f examples/inputs/emma.jpg -o 2d_sparse

demo

demo

other

The index of 81 key points in bfm is

array([[21874, 22150, 21654, 21037, 43237, 44919, 46167, 47136, 47915,
        48696, 49668, 50925, 52614, 33679, 33006, 32470, 32710, 38696,
        39393, 39783, 39988, 40155, 40894, 41060, 41268, 41662, 42368,
         8162,  8178,  8188,  8193,  6516,  7244,  8205,  9164,  9884,
         2216,  3887,  4921,  5829,  4802,  3641, 10456, 11354, 12384,
        14067, 12654, 11493,  5523,  6026,  7496,  8216,  8936, 10396,
        10796,  9556,  8837,  8237,  7637,  6916,  5910,  7385,  8224,
         9065, 10538,  8830,  8230,  7630, 21462, 38570, 39258, 39696,
        40022, 40321, 40503, 40737, 40982, 41368, 41849, 42465, 32941]])

Citation

If your work or research benefits from this repo, please cite two bibs below : ) and 🌟 this repo.

@inproceedings{guo2020towards,
    title =        {Towards Fast, Accurate and Stable 3D Dense Face Alignment},
    author =       {Guo, Jianzhu and Zhu, Xiangyu and Yang, Yang and Yang, Fan and Lei, Zhen and Li, Stan Z},
    booktitle =    {Proceedings of the European Conference on Computer Vision (ECCV)},
    year =         {2020}
}

@misc{3ddfa_cleardusk,
    author =       {Guo, Jianzhu and Zhu, Xiangyu and Lei, Zhen},
    title =        {3DDFA},
    howpublished = {\url{https://github.com/cleardusk/3DDFA}},
    year =         {2018}
}