The pytorch implementation of paper Face Alignment in Full Pose Range: A 3D Total Solution.
@article{zhu2017face,
title={Face Alignment in Full Pose Range: A 3D Total Solution},
author={Zhu, Xiangyu and Lei, Zhen and Li, Stan Z and others},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2017},
publisher={IEEE}
}
- PyTorch >= 0.4.0
- Python3.6
I strongly recommend using Python3.6 instead of older version for its better design.
First, you should download the cropped testset ALFW and ALFW-2000-3D in test.data.zip, then unzip it and put it in the root directory.
Next, run the benchmark code by providing trained model path.
I have already provided four pre-trained models in models directory. These models are trained using different loss in the first stage. The model size is about 13M due to the high efficiency of mobilenet-v1 structure.
python3 ./benchmark.py -c models/phase1_wpdc_vdc.pth.tar
The performances of pre-trained models are shown below. In the first stage, the effectiveness of different loss is in order: WPDC > VDC > PDC. While the strategy using VDC to finetune WPDC achieves the best result.
| Model | AFLW (21 pts) | AFLW 2000-3D (68 pts) |
|---|---|---|
| phase1_pdc.pth.tar | 6.956±0.981 | 5.644±1.323 |
| phase1_vdc.pth.tar | 6.717±0.924 | 5.030±1.044 |
| phase1_wpdc.pth.tar | 6.348±0.929 | 4.759±0.996 |
| phase1_wpdc_vdc.pth.tar | 5.401±0.754 | 4.252±0.976 |
[todo]