By Jianzhu Guo, Xiangyu Zhu, Yang Yang, Fan Yang, Zhen Lei and Stan Z. Li.
Introduction
This work extends 3DDFA, named 3DDFA_V2, titled In opposition to Swiftly, Correct and Stable 3D Dense Face Alignment, authorized by ECCV 2020. The supplementary area topic is right here. The gif above exhibits a demo of the monitoring result.
This repo is the legit implementation of 3DDFA_V2.
Compared to 3DDFA, 3DDFA_V2 achieves higher efficiency and steadiness. Moreover, 3DDFA_V2 contains the rapid face detector FaceBoxes as a substitute of Dlib. An easy 3D render written by c++ and cython is also incorporated. Whilst you are attracted to this repo, lawful strive it on this google colab! Welcome for precious considerations and PRs
Getting started
Requirements
Stare necessities.txt, tested on macOS and Linux platforms. Show that this repo makes spend of Python3. Basically the most valuable dependencies are PyTorch, numpy and opencv-python, and heaps others.
Utilization
- Clone this repo
git clone https://github.com/cleardusk/3DDFA_V2.git
cd 3DDFA_V2
- Discover the cython model of NMS, and Sim3DR
- Flee demos
# working on tranquil image, four alternatives: 2d_sparse, 2d_dense, 3d, depth python3 demo.py -f examples/inputs/emma.jpg # working on movies python3 demo_video.py -f examples/inputs/movies/214.avi # working on movies smoothly by taking a look ahead by `n_next` frames python3 demo_video_smooth.py -f examples/inputs/movies/214.avi
The implementation of monitoring is merely by alignment. If the top pose > 90° or the motion is just too rapid, the alignment can also fail. A threshold is ancient to trickly test the monitoring state, but it’s unstable.
It is seemingly you’ll well focus on over with demo.ipynb or google colab for the step-by-step tutorial of working on the tranquil image.
As an example, working python3 demo.py -f examples/inputs/emma.jpg -o 3d
will give the final result below:
Acknowledgement
- The FaceBoxes module is modified from FaceBoxes.PyTorch
Quotation
If your work or research advantages from this repo, please cite two bibs below : )
@inproceedings{guo2020in direction of,
title = {In opposition to Swiftly, Correct and Stable 3D Dense Face Alignment},
writer = {Guo, Jianzhu and Zhu, Xiangyu and Yang, Yang and Yang, Fan and Lei, Zhen and Li, Stan Z},
booktitle = {Complaints of the European Conference on Computer Imaginative and prescient (ECCV)},
year = {2020}
}
@misc{3ddfa_cleardusk,
writer = {Guo, Jianzhu and Zhu, Xiangyu and Lei, Zhen},
title = {3DDFA},
howpublished = {url{https://github.com/cleardusk/3DDFA}},
year = {2018}
}
Contact
Jianzhu Guo (???) [Homepage, Google Scholar]: [email protected] or [email protected].