Face Alignment in Full Pose Range: A 3D Total Solution

Introduction

This repo holds the pytorch implementation of paper Face Alignment in Full Pose Range: A 3D Total Solution and release the pre-trained first-stage pytorch models of MobileNet-V1 structure. The inference time is about 0.45ms per image on GeForce GTX TITAN X. More features will be added in future.

Several results (inferenced from model phase1_wpdc_vdc.pth.tar) are shown below.

Citation

@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}
}

Requirements

PyTorch >= 0.4.0
Python3.6

I strongly recommend using Python3.6 instead of older version for its better design.

Evaluation

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

Training