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

License: MIT stars GitHub issues GitHub repo size

<!-- By [Jianzhu Guo](https://guojianzhu.com/aboutme.html). -->

By Jianzhu Guo.

<p align="center"> <img src="samples/obama_three_styles.gif" alt="obama"> </p>

[Updates]

[Todo]

Introduction

This repo holds the pytorch improved version of the paper: Face Alignment in Full Pose Range: A 3D Total Solution. Several works beyond the original paper are added, including the real-time training, training strategies. Therefore, this repo is an improved version of the original work. As far, this repo releases the pre-trained first-stage pytorch models of MobileNet-V1 structure, the pre-processed training&testing dataset and codebase. Note that the inference time is about 0.27ms per image (input batch with 128 images as an input batch) on GeForce GTX TITAN X.

<!-- Note that if your academic work use the code of this repo, you should cite this repo not the original paper.--> <!-- One related blog will be published for some important technique details in future. --> <!-- Why not evaluate it on single image? Because most time for single image is spent on function call. The inference speed is equal to MobileNet-V1 with 120x120x3 tensor as input, therefore it is possible to convert to mobile devices. -->

This repo will keep updating in my spare time, and any meaningful issues and PR are welcomed.

Several results on ALFW-2000 dataset (inferenced from model phase1_wpdc_vdc.pth.tar) are shown below.

<p align="center"> <img src="imgs/landmark_3d.jpg" alt="Landmark 3D" width="1000px"> </p> <p align="center"> <img src="imgs/vertex_3d.jpg" alt="Vertex 3D" width="750px"> </p>

Applications & Features

1. Face Alignment

<p align="center"> <img src="samples/dapeng_3DDFA_trim.gif" alt="dapeng"> </p>

2. Face Reconstruction

<p align="center"> <img src="samples/5.png" alt="demo" width="750px"> </p>

3. 3D Pose Estimation

<p align="center"> <img src="samples/pose.png" alt="tongliya" width="750px"> </p>

4. Depth Image Estimation

<p align="center"> <img src="samples/demo_depth.jpg" alt="demo_depth" width="750px"> </p>

5. PNCC & PAF Features

<p align="center"> <img src="samples/demo_pncc_paf.jpg" alt="demo_pncc_paf" width="800px"> </p>

Getting started

Requirements

# installation structions
sudo pip3 install torch torchvision # for cpu version. more option to see https://pytorch.org
sudo pip3 install numpy scipy matplotlib
sudo pip3 install dlib==19.5.0 # 19.15+ version may cause conflict with pytorch in Linux, this may take several minutes. If 19.5 version raises errors, you may try 19.15+ version.
sudo pip3 install opencv-python
sudo pip3 install cython

In addition, I strongly recommend using Python3.6+ instead of older version for its better design.

Usage

  1. Clone this repo (this may take some time as it is a little big)

    git clone https://github.com/cleardusk/3DDFA.git  # or git@github.com:cleardusk/3DDFA.git
    cd 3DDFA
    

    Then, download dlib landmark pre-trained model in Google Drive or Baidu Yun, and put it into models directory. (To reduce this repo's size, I remove some large size binary files including this model, so you should download it : ) )

  2. Build cython module (just one line for building)

    cd utils/cython
    python3 setup.py build_ext -i
    

    This is for accelerating depth estimation and PNCC render since Python is too slow in for loop.

  3. Run the main.py with arbitrary image as input

    python3 main.py -f samples/test1.jpg
    

    If you can see these output log in terminal, you run it successfully.

    Dump tp samples/test1_0.ply
    Save 68 3d landmarks to samples/test1_0.txt
    Dump obj with sampled texture to samples/test1_0.obj
    Dump tp samples/test1_1.ply
    Save 68 3d landmarks to samples/test1_1.txt
    Dump obj with sampled texture to samples/test1_1.obj
    Dump to samples/test1_pose.jpg
    Dump to samples/test1_depth.png
    Dump to samples/test1_pncc.png
    Save visualization result to samples/test1_3DDFA.jpg
    

    Because test1.jpg has two faces, there are two .ply and .obj files (can be rendered by Meshlab or Microsoft 3D Builder) predicted. Depth, PNCC, PAF and pose estimation are all set true by default. Please run python3 main.py -h or review the code for more details.

    The 68 landmarks visualization result samples/test1_3DDFA.jpg and pose estimation result samples/test1_pose.jpg are shown below:

<p align="center"> <img src="samples/test1_3DDFA.jpg" alt="samples" width="650px"> </p> <p align="center"> <img src="samples/test1_pose.jpg" alt="samples" width="650px"> </p>
  1. Additional example

    python3 ./main.py -f samples/emma_input.jpg --bbox_init=two --dlib_bbox=false
    
<p align="center"> <img src="samples/emma_input_3DDFA.jpg" alt="samples" width="750px"> </p> <p align="center"> <img src="samples/emma_input_pose.jpg" alt="samples" width="750px"> </p>

Inference speed

CPU

Just run

python3 speed_cpu.py

On my MBP (i5-8259U CPU @ 2.30GHz on 13-inch MacBook Pro), based on PyTorch v1.1.0, with a single input, the running output is:

Inference speed: 14.50±0.11 ms
<!-- [speed_cpu.py](./speed_cpu.py) -->

GPU

When input batch size is 128, the total inference time of MobileNet-V1 takes about 34.7ms. The average speed is about 0.27ms/pic.

<p align="center"> <img src="imgs/inference_speed.png" alt="Inference speed" width="600px"> </p>

Training details

The training scripts lie in training directory. The related resources are in below table.

DataDownload LinkDescription
train.configsBaiduYun or Google Drive, 217MThe directory containing 3DMM params and filelists of training dataset
train_aug_120x120.zipBaiduYun or Google Drive, 2.15GThe cropped images of augmentation training dataset
test.data.zipBaiduYun or Google Drive, 151MThe cropped images of AFLW and ALFW-2000-3D testset

After preparing the training dataset and configuration files, go into training directory and run the bash scripts to train. train_wpdc.sh, train_vdc.sh and train_pdc.sh are examples of training scripts. After configuring the training and testing sets, just run them for training. Take train_wpdc.sh for example as below:

#!/usr/bin/env bash

LOG_ALIAS=$1
LOG_DIR="logs"
mkdir -p ${LOG_DIR}

LOG_FILE="${LOG_DIR}/${LOG_ALIAS}_`date +'%Y-%m-%d_%H:%M.%S'`.log"
#echo $LOG_FILE

./train.py --arch="mobilenet_1" \
    --start-epoch=1 \
    --loss=wpdc \
    --snapshot="snapshot/phase1_wpdc" \
    --param-fp-train='../train.configs/param_all_norm.pkl' \
    --param-fp-val='../train.configs/param_all_norm_val.pkl' \
    --warmup=5 \
    --opt-style=resample \
    --resample-num=132 \
    --batch-size=512 \
    --base-lr=0.02 \
    --epochs=50 \
    --milestones=30,40 \
    --print-freq=50 \
    --devices-id=0,1 \
    --workers=8 \
    --filelists-train="../train.configs/train_aug_120x120.list.train" \
    --filelists-val="../train.configs/train_aug_120x120.list.val" \
    --root="/path/to//train_aug_120x120" \
    --log-file="${LOG_FILE}"

The specific training parameters are all presented in bash scripts, including learning rate, mini-batch size, epochs and so on.

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 five pre-trained models in models directory (seen in below table). 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.

ModelAFLW (21 pts)AFLW 2000-3D (68 pts)Download Link
phase1_pdc.pth.tar6.956±0.9815.644±1.323Baidu Yun or Google Drive
phase1_vdc.pth.tar6.717±0.9245.030±1.044Baidu Yun or Google Drive
phase1_wpdc.pth.tar6.348±0.9294.759±0.996Baidu Yun or Google Drive
phase1_wpdc_vdc.pth.tar5.401±0.7544.252±0.976In this repo.

About the performance

Believe me that the framework of this repo can achieve better performance than PRNet without increasing any computation budget. Related work is under review and code will be released upon acceptance.

FQA

  1. Face bounding box initialization

    The original paper shows that using detected bounding box instead of ground truth box will cause a little performance drop. Thus the current face cropping method is robustest. Quantitative results are shown in below table.

<p align="center"> <img src="imgs/bouding_box_init.png" alt="bounding box" width="500px"> </p>
  1. Face reconstruction

    The texture of non-visible area is distorted due to self-occlusion, therefore the non-visible face region may appear strange (a little horrible).

  2. About shape and expression parameters clipping

    The parameters clipping accelerates the training and reconstruction, but degrades the accuracy especially the details like closing eyes. Below is an image, with parameters dimension 40+10, 60+29 and 199+29 (the original one). Compared to shape, expression clipping has more effect on reconstruction accuracy when emotion is involved. Therefore, you can choose a trade-off between the speed/parameter-size and the accuracy. A recommendation of clipping trade-off is 60+29.

<p align="center"> <img src="imgs/params_clip.jpg" alt="bounding box" width="600px"> </p>

Acknowledgement

Thanks for your interest in this repo. If your work or research benefits from this repo, star it 😃

Welcome to focus on my 3D face related works: MeGlass and Face Anti-Spoofing.

Citation

If your work benefits from this repo, please cite three bibs below.

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

@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}
}

@article{zhu2017face,
  title=      {Face alignment in full pose range: A 3d total solution},
  author=     {Zhu, Xiangyu and Liu, Xiaoming and Lei, Zhen and Li, Stan Z},
  journal=    {IEEE transactions on pattern analysis and machine intelligence},
  year=       {2017},
  publisher=  {IEEE}
}

Contact

Jianzhu Guo (郭建珠) [Homepage, Google Scholar]: jianzhu.guo@nlpr.ia.ac.cn or guojianzhu1994@foxmail.com.