Synthesizing Normalized Faces From Facial Identity Features (Additional Results B.3 Section Implementation Only)
This repository provides a Python implementation of the CVPR 2017 Paper - Synthesizing Normalized Faces From Facial Identity Features (Additional Results B.3 Implementation Only). Only Fitting Texture part is implemented which is mentioned in Additional Results Section B.3 of the paper.
Synthesizing Normalized Faces From Facial Identity Features
git clone https://github.com/nabeel3133/3D-texture-fitting.git
cd 3D-texture-fitting
- BFM09: Basel Face Model 2009
- After you have acquired BFM, extract the BaselFaceModel.tgz and go to
PublicMM1
folder, copy01_MorphableModel.mat
,BFM_exp_idx.mat
and paste it in./3D-texture-fitting/configs
folder.
- After you have acquired BFM, extract the BaselFaceModel.tgz and go to
Run the main.py
with obj output from ddfa as input
python main.py -o ./samples/test.obj
If you can see the following output log in terminal, you ran it successfully.
BFM Mapping for Texture Prediction Started...
BFM Mapping for Texture Prediction Completed
Predicting Ear and Neck Texture...
Predicting Ear and Neck Texture Completed
Dump to ./samples/output.obj
Dump to ./samples/output.ply
Two output files (obj and ply) will be saved in 3D-texture-fitting/samples
folder with the name output.obj
and output.ply
which can be redered by Meshlab or Microsoft 3D Builder.
If this work is useful for your research or if you use this implementation in your academic projects, please cite the following papers:
@misc{cole2017synthesizing,
title={Synthesizing Normalized Faces from Facial Identity Features},
author={Forrester Cole and David Belanger and Dilip Krishnan and Aaron Sarna and Inbar Mosseri and William T. Freeman},
year={2017},
eprint={1701.04851},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{3ddfa_cleardusk,
author = {Jianzhu Guo, Xiangyu Zhu and Zhen Lei},
title = {3DDFA},
howpublished = {\url{https://github.com/cleardusk/3DDFA}},
year = {2018}
}