《Deep Single Portrait Image Relighting》(ICCV 2019)

Ratio Image Based Rendering for Deep Single-Image Portrait Relighting [Project Page]

This is part of the Deep Portrait Relighting project. If you find this project useful, please cite the paper:

@InProceedings{DPR, 
  title={Deep Single Portrait Image Relighting},
  author = {Hao Zhou and Sunil Hadap and Kalyan Sunkavalli and David W. Jacobs},
  booktitle={International Conference on Computer Vision (ICCV)},
  year={2019}
}

NOTE:

This code is not optimized and may not be well organized.

Dependences:

3DDFA: https://github.com/cleardusk/3DDFA (download the code and put it in useful_code, follow the instruction to download model and setup the code)

Environment setup:

I use miniconda to setup virtual environment

• Create a virtual enviroment named RI_render (you can choose your own name): conda create -n RI_render python=3.6
• Install pytorch: conda install pytorch torchvision cudatoolkit=9.2 -c pytorch -n RI_render
• Install dlib: conda install -c conda-forge dlib -n RI_render
• Install opencv: conda install -n RI_render -c conda-forge opencv
• Install scipy: conda install -n RI_render -c conda-forge scipy
• Install matplotlib: conda install -n RI_render -c conda-forge matplotlib
• Install cython: conda install -n RI_render -c anaconda cython
• Compile 3DDFA as mentioned in the github webpage
• Compile cython in utils/cython, follow the readme file
• Install Delaunay Triangulation:
  • Download Berkeley triangulation code from (http://www.cs.cmu.edu/~quake/triangle.html)
  • Unzip it into useful_code/triangle_berkeley/: unzip -d /useful_code/triangle_berkeley
• Install libigl:
  • Download libig from https://github.com/libigl/libigl
  • Go to utils/libigl_arap, in my_arap.sh change the path of eigen and libigl
  • Run bash my_arap.sh
• Install shtools: https://github.com/SHTOOLS/SHTOOLS
• Install cvxpy: conda install -c conda-forge cvxpy

Steps for rendering

1. fitting 3DDFA: run bash run_fit.sh, will generate several files in result: *_3DDFA.png: draw 2D landmark on face *_depth.png: depth image *_detected.txt: detected 2D landmark on faces *_project.txt: projected 3D landmark *.obj: fitted mesh

2. run bash run_render.sh generate albedo, normal, uv map and semantic segmentation: *_new.obj: obj file for rendering in render: *.png show generate images *.npy show original file of albedo, normal, uv map and semantic segmentation. NOTE: if you can install OpenEXR, you can save npy as .exr file

3. run bash run_node.sh Apply arap to further align faces in render: generate arap.obj an object of arap algorithm *.node and *.ele temperal files for applying arap

4. run bash run_warp.sh create warped albedo, normal, semantic segmentation in result/warp:

5. run bash run_fillHoles.sh remove ear and neck region and fill in holes in generated normal map: create full_normal_faceRegion_faceBoundary_extend.npy and full_normal_faceRegion_faceBoundary_extend.png in result/warp

6. run bash run_relight.sh relighting faces download our processed bip2017 lighting through (https://drive.google.com/open?id=1l0SiR10jBqACiOeAvsXSXAufUtZ-VhxC), change line 155 in script_relighting.py to poit to the lighting folder Apply face semantic segmentation to get skin region of the face: https://github.com/Liusifei/Face_Parsing_2016 save the results in folder face_parsing/ (examples are shown in face_parsing, you can also skip this by adapting the code of script_relighting.py)